{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"frcnn_train_vgg.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"metadata":{"id":"kauOILHSqXWw","colab_type":"code","outputId":"abce5476-5b42-44c5-c0ec-2eb26f5fcfa7","executionInfo":{"status":"ok","timestamp":1542543682973,"user_tz":-480,"elapsed":31637,"user":{"displayName":"Yinghan Xu","photoUrl":"https://lh3.googleusercontent.com/-ZH_vkc0a6g0/AAAAAAAAAAI/AAAAAAAAAAc/x8TPjkqmxys/s64/photo.jpg","userId":"13488364327507272606"}},"colab":{"base_uri":"https://localhost:8080/","height":191}},"cell_type":"code","source":["# from google.colab import drive\n","# drive.mount('/content/drive')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"metadata":{"id":"_fzxugFjqc2T","colab_type":"code","outputId":"2783a637-6e63-4f97-f31f-33e1f5f2f9bb","executionInfo":{"status":"ok","timestamp":1542543688169,"user_tz":-480,"elapsed":2078,"user":{"displayName":"Yinghan Xu","photoUrl":"https://lh3.googleusercontent.com/-ZH_vkc0a6g0/AAAAAAAAAAI/AAAAAAAAAAc/x8TPjkqmxys/s64/photo.jpg","userId":"13488364327507272606"}},"colab":{"base_uri":"https://localhost:8080/","height":65}},"cell_type":"code","source":["!ls"],"execution_count":0,"outputs":[{"output_type":"stream","text":["drive  sample_data\n"],"name":"stdout"}]},{"metadata":{"id":"UauKNtFRqydu","colab_type":"text"},"cell_type":"markdown","source":["### Import libs"]},{"metadata":{"id":"qSbY3Amlqpkh","colab_type":"code","outputId":"90795cb8-8374-4711-a036-e005c4c7b9d2","executionInfo":{"status":"ok","timestamp":1542543695321,"user_tz":-480,"elapsed":2189,"user":{"displayName":"Yinghan Xu","photoUrl":"https://lh3.googleusercontent.com/-ZH_vkc0a6g0/AAAAAAAAAAI/AAAAAAAAAAc/x8TPjkqmxys/s64/photo.jpg","userId":"13488364327507272606"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["from __future__ import division\n","from __future__ import print_function\n","from __future__ import absolute_import\n","import random\n","import pprint\n","import sys\n","import time\n","import numpy as np\n","from optparse import OptionParser\n","import pickle\n","import math\n","import cv2\n","import copy\n","from matplotlib import pyplot as plt\n","import tensorflow as tf\n","import pandas as pd\n","import os\n","\n","from sklearn.metrics import average_precision_score\n","\n","from keras import backend as K\n","from keras.optimizers import Adam, SGD, RMSprop\n","from keras.layers import Flatten, Dense, Input, Conv2D, MaxPooling2D, Dropout\n","from keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, TimeDistributed\n","from keras.engine.topology import get_source_inputs\n","from keras.utils import layer_utils\n","from keras.utils.data_utils import get_file\n","from keras.objectives import categorical_crossentropy\n","\n","from keras.models import Model\n","from keras.utils import generic_utils\n","from keras.engine import Layer, InputSpec\n","from keras import initializers, regularizers"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"metadata":{"id":"WrH5i5mmrDWY","colab_type":"text"},"cell_type":"markdown","source":["#### Config setting"]},{"metadata":{"id":"DvJm0FFRsyVu","colab_type":"code","colab":{}},"cell_type":"code","source":["class Config:\n","\n","\tdef __init__(self):\n","\n","\t\t# Print the process or not\n","\t\tself.verbose = True\n","\n","\t\t# Name of base network\n","\t\tself.network = 'vgg'\n","\n","\t\t# Setting for data augmentation\n","\t\tself.use_horizontal_flips = False\n","\t\tself.use_vertical_flips = False\n","\t\tself.rot_90 = False\n","\n","\t\t# Anchor box scales\n","    # Note that if im_size is smaller, anchor_box_scales should be scaled\n","    # Original anchor_box_scales in the paper is [128, 256, 512]\n","\t\tself.anchor_box_scales = [64, 128, 256] \n","\n","\t\t# Anchor box ratios\n","\t\tself.anchor_box_ratios = [[1, 1], [1./math.sqrt(2), 2./math.sqrt(2)], [2./math.sqrt(2), 1./math.sqrt(2)]]\n","\n","\t\t# Size to resize the smallest side of the image\n","\t\t# Original setting in paper is 600. Set to 300 in here to save training time\n","\t\tself.im_size = 300\n","\n","\t\t# image channel-wise mean to subtract\n","\t\tself.img_channel_mean = [103.939, 116.779, 123.68]\n","\t\tself.img_scaling_factor = 1.0\n","\n","\t\t# number of ROIs at once\n","\t\tself.num_rois = 4\n","\n","\t\t# stride at the RPN (this depends on the network configuration)\n","\t\tself.rpn_stride = 16\n","\n","\t\tself.balanced_classes = False\n","\n","\t\t# scaling the stdev\n","\t\tself.std_scaling = 4.0\n","\t\tself.classifier_regr_std = [8.0, 8.0, 4.0, 4.0]\n","\n","\t\t# overlaps for RPN\n","\t\tself.rpn_min_overlap = 0.3\n","\t\tself.rpn_max_overlap = 0.7\n","\n","\t\t# overlaps for classifier ROIs\n","\t\tself.classifier_min_overlap = 0.1\n","\t\tself.classifier_max_overlap = 0.5\n","\n","\t\t# placeholder for the class mapping, automatically generated by the parser\n","\t\tself.class_mapping = None\n","\n","\t\tself.model_path = None"],"execution_count":0,"outputs":[]},{"metadata":{"id":"o0bIjlycyR9_","colab_type":"text"},"cell_type":"markdown","source":["#### Parser the data from annotation file"]},{"metadata":{"id":"vc89E9uAydTX","colab_type":"code","colab":{}},"cell_type":"code","source":["def get_data(input_path):\n","\t\"\"\"Parse the data from annotation file\n","\t\n","\tArgs:\n","\t\tinput_path: annotation file path\n","\n","\tReturns:\n","\t\tall_data: list(filepath, width, height, list(bboxes))\n","\t\tclasses_count: dict{key:class_name, value:count_num} \n","\t\t\te.g. {'Car': 2383, 'Mobile phone': 1108, 'Person': 3745}\n","\t\tclass_mapping: dict{key:class_name, value: idx}\n","\t\t\te.g. {'Car': 0, 'Mobile phone': 1, 'Person': 2}\n","\t\"\"\"\n","\tfound_bg = False\n","\tall_imgs = {}\n","\n","\tclasses_count = {}\n","\n","\tclass_mapping = {}\n","\n","\tvisualise = True\n","\n","\ti = 1\n","\t\n","\twith open(input_path,'r') as f:\n","\n","\t\tprint('Parsing annotation files')\n","\n","\t\tfor line in f:\n","\n","\t\t\t# Print process\n","\t\t\tsys.stdout.write('\\r'+'idx=' + str(i))\n","\t\t\ti += 1\n","\n","\t\t\tline_split = line.strip().split(',')\n","\n","\t\t\t# Make sure the info saved in annotation file matching the format (path_filename, x1, y1, x2, y2, class_name)\n","\t\t\t# Note:\n","\t\t\t#\tOne path_filename might has several classes (class_name)\n","\t\t\t#\tx1, y1, x2, y2 are the pixel value of the origial image, not the ratio value\n","\t\t\t#\t(x1, y1) top left coordinates; (x2, y2) bottom right coordinates\n","\t\t\t#   x1,y1-------------------\n","\t\t\t#\t|\t\t\t\t\t\t|\n","\t\t\t#\t|\t\t\t\t\t\t|\n","\t\t\t#\t|\t\t\t\t\t\t|\n","\t\t\t#\t|\t\t\t\t\t\t|\n","\t\t\t#\t---------------------x2,y2\n","\n","\t\t\t(filename,x1,y1,x2,y2,class_name) = line_split\n","\n","\t\t\tif class_name not in classes_count:\n","\t\t\t\tclasses_count[class_name] = 1\n","\t\t\telse:\n","\t\t\t\tclasses_count[class_name] += 1\n","\n","\t\t\tif class_name not in class_mapping:\n","\t\t\t\tif class_name == 'bg' and found_bg == False:\n","\t\t\t\t\tprint('Found class name with special name bg. Will be treated as a background region (this is usually for hard negative mining).')\n","\t\t\t\t\tfound_bg = True\n","\t\t\t\tclass_mapping[class_name] = len(class_mapping)\n","\n","\t\t\tif filename not in all_imgs:\n","\t\t\t\tall_imgs[filename] = {}\n","\t\t\t\t\n","\t\t\t\timg = cv2.imread(filename)\n","\t\t\t\t(rows,cols) = img.shape[:2]\n","\t\t\t\tall_imgs[filename]['filepath'] = filename\n","\t\t\t\tall_imgs[filename]['width'] = cols\n","\t\t\t\tall_imgs[filename]['height'] = rows\n","\t\t\t\tall_imgs[filename]['bboxes'] = []\n","\t\t\t\t# if np.random.randint(0,6) > 0:\n","\t\t\t\t# \tall_imgs[filename]['imageset'] = 'trainval'\n","\t\t\t\t# else:\n","\t\t\t\t# \tall_imgs[filename]['imageset'] = 'test'\n","\n","\t\t\tall_imgs[filename]['bboxes'].append({'class': class_name, 'x1': int(x1), 'x2': int(x2), 'y1': int(y1), 'y2': int(y2)})\n","\n","\n","\t\tall_data = []\n","\t\tfor key in all_imgs:\n","\t\t\tall_data.append(all_imgs[key])\n","\t\t\n","\t\t# make sure the bg class is last in the list\n","\t\tif found_bg:\n","\t\t\tif class_mapping['bg'] != len(class_mapping) - 1:\n","\t\t\t\tkey_to_switch = [key for key in class_mapping.keys() if class_mapping[key] == len(class_mapping)-1][0]\n","\t\t\t\tval_to_switch = class_mapping['bg']\n","\t\t\t\tclass_mapping['bg'] = len(class_mapping) - 1\n","\t\t\t\tclass_mapping[key_to_switch] = val_to_switch\n","\t\t\n","\t\treturn all_data, classes_count, class_mapping"],"execution_count":0,"outputs":[]},{"metadata":{"id":"oFvqGs4acGWl","colab_type":"text"},"cell_type":"markdown","source":["#### Define ROI Pooling Convolutional Layer"]},{"metadata":{"id":"6l32Q85kcMpB","colab_type":"code","colab":{}},"cell_type":"code","source":["class RoiPoolingConv(Layer):\n","    '''ROI pooling layer for 2D inputs.\n","    See Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition,\n","    K. He, X. Zhang, S. Ren, J. Sun\n","    # Arguments\n","        pool_size: int\n","            Size of pooling region to use. pool_size = 7 will result in a 7x7 region.\n","        num_rois: number of regions of interest to be used\n","    # Input shape\n","        list of two 4D tensors [X_img,X_roi] with shape:\n","        X_img:\n","        `(1, rows, cols, channels)`\n","        X_roi:\n","        `(1,num_rois,4)` list of rois, with ordering (x,y,w,h)\n","    # Output shape\n","        3D tensor with shape:\n","        `(1, num_rois, channels, pool_size, pool_size)`\n","    '''\n","    def __init__(self, pool_size, num_rois, **kwargs):\n","\n","        self.dim_ordering = K.image_dim_ordering()\n","        self.pool_size = pool_size\n","        self.num_rois = num_rois\n","\n","        super(RoiPoolingConv, self).__init__(**kwargs)\n","\n","    def build(self, input_shape):\n","        self.nb_channels = input_shape[0][3]   \n","\n","    def compute_output_shape(self, input_shape):\n","        return None, self.num_rois, self.pool_size, self.pool_size, self.nb_channels\n","\n","    def call(self, x, mask=None):\n","\n","        assert(len(x) == 2)\n","\n","        # x[0] is image with shape (rows, cols, channels)\n","        img = x[0]\n","\n","        # x[1] is roi with shape (num_rois,4) with ordering (x,y,w,h)\n","        rois = x[1]\n","\n","        input_shape = K.shape(img)\n","\n","        outputs = []\n","\n","        for roi_idx in range(self.num_rois):\n","\n","            x = rois[0, roi_idx, 0]\n","            y = rois[0, roi_idx, 1]\n","            w = rois[0, roi_idx, 2]\n","            h = rois[0, roi_idx, 3]\n","\n","            x = K.cast(x, 'int32')\n","            y = K.cast(y, 'int32')\n","            w = K.cast(w, 'int32')\n","            h = K.cast(h, 'int32')\n","\n","            # Resized roi of the image to pooling size (7x7)\n","            rs = tf.image.resize_images(img[:, y:y+h, x:x+w, :], (self.pool_size, self.pool_size))\n","            outputs.append(rs)\n","                \n","\n","        final_output = K.concatenate(outputs, axis=0)\n","\n","        # Reshape to (1, num_rois, pool_size, pool_size, nb_channels)\n","        # Might be (1, 4, 7, 7, 3)\n","        final_output = K.reshape(final_output, (1, self.num_rois, self.pool_size, self.pool_size, self.nb_channels))\n","\n","        # permute_dimensions is similar to transpose\n","        final_output = K.permute_dimensions(final_output, (0, 1, 2, 3, 4))\n","\n","        return final_output\n","    \n","    \n","    def get_config(self):\n","        config = {'pool_size': self.pool_size,\n","                  'num_rois': self.num_rois}\n","        base_config = super(RoiPoolingConv, self).get_config()\n","        return dict(list(base_config.items()) + list(config.items()))"],"execution_count":0,"outputs":[]},{"metadata":{"id":"Mf2taA29RFNs","colab_type":"text"},"cell_type":"markdown","source":["#### Vgg-16 model"]},{"metadata":{"id":"WaBQfl4XRJY3","colab_type":"code","colab":{}},"cell_type":"code","source":["def get_img_output_length(width, height):\n","    def get_output_length(input_length):\n","        return input_length//16\n","\n","    return get_output_length(width), get_output_length(height)    \n","\n","def nn_base(input_tensor=None, trainable=False):\n","\n","\n","    input_shape = (None, None, 3)\n","\n","    if input_tensor is None:\n","        img_input = Input(shape=input_shape)\n","    else:\n","        if not K.is_keras_tensor(input_tensor):\n","            img_input = Input(tensor=input_tensor, shape=input_shape)\n","        else:\n","            img_input = input_tensor\n","\n","    bn_axis = 3\n","\n","    # Block 1\n","    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)\n","    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)\n","    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\n","\n","    # Block 2\n","    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)\n","    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)\n","    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\n","\n","    # Block 3\n","    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)\n","    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)\n","    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)\n","    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\n","\n","    # Block 4\n","    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)\n","    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)\n","    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)\n","    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\n","\n","    # Block 5\n","    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)\n","    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)\n","    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)\n","    # x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\n","\n","    return x"],"execution_count":0,"outputs":[]},{"metadata":{"id":"xcOi5MIMVJpU","colab_type":"text"},"cell_type":"markdown","source":["####  RPN layer"]},{"metadata":{"id":"gsuV21vpRczQ","colab_type":"code","colab":{}},"cell_type":"code","source":["def rpn_layer(base_layers, num_anchors):\n","    \"\"\"Create a rpn layer\n","        Step1: Pass through the feature map from base layer to a 3x3 512 channels convolutional layer\n","                Keep the padding 'same' to preserve the feature map's size\n","        Step2: Pass the step1 to two (1,1) convolutional layer to replace the fully connected layer\n","                classification layer: num_anchors (9 in here) channels for 0, 1 sigmoid activation output\n","                regression layer: num_anchors*4 (36 in here) channels for computing the regression of bboxes with linear activation\n","    Args:\n","        base_layers: vgg in here\n","        num_anchors: 9 in here\n","\n","    Returns:\n","        [x_class, x_regr, base_layers]\n","        x_class: classification for whether it's an object\n","        x_regr: bboxes regression\n","        base_layers: vgg in here\n","    \"\"\"\n","    x = Conv2D(512, (3, 3), padding='same', activation='relu', kernel_initializer='normal', name='rpn_conv1')(base_layers)\n","\n","    x_class = Conv2D(num_anchors, (1, 1), activation='sigmoid', kernel_initializer='uniform', name='rpn_out_class')(x)\n","    x_regr = Conv2D(num_anchors * 4, (1, 1), activation='linear', kernel_initializer='zero', name='rpn_out_regress')(x)\n","\n","    return [x_class, x_regr, base_layers]"],"execution_count":0,"outputs":[]},{"metadata":{"id":"0fBt9xNFWsKS","colab_type":"text"},"cell_type":"markdown","source":["####  Classifier layer"]},{"metadata":{"id":"0PKSPLRLWwMz","colab_type":"code","colab":{}},"cell_type":"code","source":["def classifier_layer(base_layers, input_rois, num_rois, nb_classes = 4):\n","    \"\"\"Create a classifier layer\n","    \n","    Args:\n","        base_layers: vgg\n","        input_rois: `(1,num_rois,4)` list of rois, with ordering (x,y,w,h)\n","        num_rois: number of rois to be processed in one time (4 in here)\n","\n","    Returns:\n","        list(out_class, out_regr)\n","        out_class: classifier layer output\n","        out_regr: regression layer output\n","    \"\"\"\n","\n","    input_shape = (num_rois,7,7,512)\n","\n","    pooling_regions = 7\n","\n","    # out_roi_pool.shape = (1, num_rois, channels, pool_size, pool_size)\n","    # num_rois (4) 7x7 roi pooling\n","    out_roi_pool = RoiPoolingConv(pooling_regions, num_rois)([base_layers, input_rois])\n","\n","    # Flatten the convlutional layer and connected to 2 FC and 2 dropout\n","    out = TimeDistributed(Flatten(name='flatten'))(out_roi_pool)\n","    out = TimeDistributed(Dense(4096, activation='relu', name='fc1'))(out)\n","    out = TimeDistributed(Dropout(0.5))(out)\n","    out = TimeDistributed(Dense(4096, activation='relu', name='fc2'))(out)\n","    out = TimeDistributed(Dropout(0.5))(out)\n","\n","    # There are two output layer\n","    # out_class: softmax acivation function for classify the class name of the object\n","    # out_regr: linear activation function for bboxes coordinates regression\n","    out_class = TimeDistributed(Dense(nb_classes, activation='softmax', kernel_initializer='zero'), name='dense_class_{}'.format(nb_classes))(out)\n","    # note: no regression target for bg class\n","    out_regr = TimeDistributed(Dense(4 * (nb_classes-1), activation='linear', kernel_initializer='zero'), name='dense_regress_{}'.format(nb_classes))(out)\n","\n","    return [out_class, out_regr]"],"execution_count":0,"outputs":[]},{"metadata":{"id":"WMev3UMadCzJ","colab_type":"text"},"cell_type":"markdown","source":["#### Calculate IoU (Intersection of Union)"]},{"metadata":{"id":"Jy5iIBYgdCJD","colab_type":"code","colab":{}},"cell_type":"code","source":["def union(au, bu, area_intersection):\n","\tarea_a = (au[2] - au[0]) * (au[3] - au[1])\n","\tarea_b = (bu[2] - bu[0]) * (bu[3] - bu[1])\n","\tarea_union = area_a + area_b - area_intersection\n","\treturn area_union\n","\n","\n","def intersection(ai, bi):\n","\tx = max(ai[0], bi[0])\n","\ty = max(ai[1], bi[1])\n","\tw = min(ai[2], bi[2]) - x\n","\th = min(ai[3], bi[3]) - y\n","\tif w < 0 or h < 0:\n","\t\treturn 0\n","\treturn w*h\n","\n","\n","def iou(a, b):\n","\t# a and b should be (x1,y1,x2,y2)\n","\n","\tif a[0] >= a[2] or a[1] >= a[3] or b[0] >= b[2] or b[1] >= b[3]:\n","\t\treturn 0.0\n","\n","\tarea_i = intersection(a, b)\n","\tarea_u = union(a, b, area_i)\n","\n","\treturn float(area_i) / float(area_u + 1e-6)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"rcRlzqZudKkd","colab_type":"text"},"cell_type":"markdown","source":["#### Calculate the rpn for all anchors of all images"]},{"metadata":{"id":"daPsCZtrdK3S","colab_type":"code","colab":{}},"cell_type":"code","source":["def calc_rpn(C, img_data, width, height, resized_width, resized_height, img_length_calc_function):\n","\t\"\"\"(Important part!) Calculate the rpn for all anchors \n","\t\tIf feature map has shape 38x50=1900, there are 1900x9=17100 potential anchors\n","\t\n","\tArgs:\n","\t\tC: config\n","\t\timg_data: augmented image data\n","\t\twidth: original image width (e.g. 600)\n","\t\theight: original image height (e.g. 800)\n","\t\tresized_width: resized image width according to C.im_size (e.g. 300)\n","\t\tresized_height: resized image height according to C.im_size (e.g. 400)\n","\t\timg_length_calc_function: function to calculate final layer's feature map (of base model) size according to input image size\n","\n","\tReturns:\n","\t\ty_rpn_cls: list(num_bboxes, y_is_box_valid + y_rpn_overlap)\n","\t\t\ty_is_box_valid: 0 or 1 (0 means the box is invalid, 1 means the box is valid)\n","\t\t\ty_rpn_overlap: 0 or 1 (0 means the box is not an object, 1 means the box is an object)\n","\t\ty_rpn_regr: list(num_bboxes, 4*y_rpn_overlap + y_rpn_regr)\n","\t\t\ty_rpn_regr: x1,y1,x2,y2 bunding boxes coordinates\n","\t\"\"\"\n","\tdownscale = float(C.rpn_stride) \n","\tanchor_sizes = C.anchor_box_scales   # 128, 256, 512\n","\tanchor_ratios = C.anchor_box_ratios  # 1:1, 1:2*sqrt(2), 2*sqrt(2):1\n","\tnum_anchors = len(anchor_sizes) * len(anchor_ratios) # 3x3=9\n","\n","\t# calculate the output map size based on the network architecture\n","\t(output_width, output_height) = img_length_calc_function(resized_width, resized_height)\n","\n","\tn_anchratios = len(anchor_ratios)    # 3\n","\t\n","\t# initialise empty output objectives\n","\ty_rpn_overlap = np.zeros((output_height, output_width, num_anchors))\n","\ty_is_box_valid = np.zeros((output_height, output_width, num_anchors))\n","\ty_rpn_regr = np.zeros((output_height, output_width, num_anchors * 4))\n","\n","\tnum_bboxes = len(img_data['bboxes'])\n","\n","\tnum_anchors_for_bbox = np.zeros(num_bboxes).astype(int)\n","\tbest_anchor_for_bbox = -1*np.ones((num_bboxes, 4)).astype(int)\n","\tbest_iou_for_bbox = np.zeros(num_bboxes).astype(np.float32)\n","\tbest_x_for_bbox = np.zeros((num_bboxes, 4)).astype(int)\n","\tbest_dx_for_bbox = np.zeros((num_bboxes, 4)).astype(np.float32)\n","\n","\t# get the GT box coordinates, and resize to account for image resizing\n","\tgta = np.zeros((num_bboxes, 4))\n","\tfor bbox_num, bbox in enumerate(img_data['bboxes']):\n","\t\t# get the GT box coordinates, and resize to account for image resizing\n","\t\tgta[bbox_num, 0] = bbox['x1'] * (resized_width / float(width))\n","\t\tgta[bbox_num, 1] = bbox['x2'] * (resized_width / float(width))\n","\t\tgta[bbox_num, 2] = bbox['y1'] * (resized_height / float(height))\n","\t\tgta[bbox_num, 3] = bbox['y2'] * (resized_height / float(height))\n","\t\n","\t# rpn ground truth\n","\n","\tfor anchor_size_idx in range(len(anchor_sizes)):\n","\t\tfor anchor_ratio_idx in range(n_anchratios):\n","\t\t\tanchor_x = anchor_sizes[anchor_size_idx] * anchor_ratios[anchor_ratio_idx][0]\n","\t\t\tanchor_y = anchor_sizes[anchor_size_idx] * anchor_ratios[anchor_ratio_idx][1]\t\n","\t\t\t\n","\t\t\tfor ix in range(output_width):\t\t\t\t\t\n","\t\t\t\t# x-coordinates of the current anchor box\t\n","\t\t\t\tx1_anc = downscale * (ix + 0.5) - anchor_x / 2\n","\t\t\t\tx2_anc = downscale * (ix + 0.5) + anchor_x / 2\t\n","\t\t\t\t\n","\t\t\t\t# ignore boxes that go across image boundaries\t\t\t\t\t\n","\t\t\t\tif x1_anc < 0 or x2_anc > resized_width:\n","\t\t\t\t\tcontinue\n","\t\t\t\t\t\n","\t\t\t\tfor jy in range(output_height):\n","\n","\t\t\t\t\t# y-coordinates of the current anchor box\n","\t\t\t\t\ty1_anc = downscale * (jy + 0.5) - anchor_y / 2\n","\t\t\t\t\ty2_anc = downscale * (jy + 0.5) + anchor_y / 2\n","\n","\t\t\t\t\t# ignore boxes that go across image boundaries\n","\t\t\t\t\tif y1_anc < 0 or y2_anc > resized_height:\n","\t\t\t\t\t\tcontinue\n","\n","\t\t\t\t\t# bbox_type indicates whether an anchor should be a target\n","\t\t\t\t\t# Initialize with 'negative'\n","\t\t\t\t\tbbox_type = 'neg'\n","\n","\t\t\t\t\t# this is the best IOU for the (x,y) coord and the current anchor\n","\t\t\t\t\t# note that this is different from the best IOU for a GT bbox\n","\t\t\t\t\tbest_iou_for_loc = 0.0\n","\n","\t\t\t\t\tfor bbox_num in range(num_bboxes):\n","\t\t\t\t\t\t\n","\t\t\t\t\t\t# get IOU of the current GT box and the current anchor box\n","\t\t\t\t\t\tcurr_iou = iou([gta[bbox_num, 0], gta[bbox_num, 2], gta[bbox_num, 1], gta[bbox_num, 3]], [x1_anc, y1_anc, x2_anc, y2_anc])\n","\t\t\t\t\t\t# calculate the regression targets if they will be needed\n","\t\t\t\t\t\tif curr_iou > best_iou_for_bbox[bbox_num] or curr_iou > C.rpn_max_overlap:\n","\t\t\t\t\t\t\tcx = (gta[bbox_num, 0] + gta[bbox_num, 1]) / 2.0\n","\t\t\t\t\t\t\tcy = (gta[bbox_num, 2] + gta[bbox_num, 3]) / 2.0\n","\t\t\t\t\t\t\tcxa = (x1_anc + x2_anc)/2.0\n","\t\t\t\t\t\t\tcya = (y1_anc + y2_anc)/2.0\n","\n","\t\t\t\t\t\t\t# x,y are the center point of ground-truth bbox\n","\t\t\t\t\t\t\t# xa,ya are the center point of anchor bbox (xa=downscale * (ix + 0.5); ya=downscale * (iy+0.5))\n","\t\t\t\t\t\t\t# w,h are the width and height of ground-truth bbox\n","\t\t\t\t\t\t\t# wa,ha are the width and height of anchor bboxe\n","\t\t\t\t\t\t\t# tx = (x - xa) / wa\n","\t\t\t\t\t\t\t# ty = (y - ya) / ha\n","\t\t\t\t\t\t\t# tw = log(w / wa)\n","\t\t\t\t\t\t\t# th = log(h / ha)\n","\t\t\t\t\t\t\ttx = (cx - cxa) / (x2_anc - x1_anc)\n","\t\t\t\t\t\t\tty = (cy - cya) / (y2_anc - y1_anc)\n","\t\t\t\t\t\t\ttw = np.log((gta[bbox_num, 1] - gta[bbox_num, 0]) / (x2_anc - x1_anc))\n","\t\t\t\t\t\t\tth = np.log((gta[bbox_num, 3] - gta[bbox_num, 2]) / (y2_anc - y1_anc))\n","\t\t\t\t\t\t\n","\t\t\t\t\t\tif img_data['bboxes'][bbox_num]['class'] != 'bg':\n","\n","\t\t\t\t\t\t\t# all GT boxes should be mapped to an anchor box, so we keep track of which anchor box was best\n","\t\t\t\t\t\t\tif curr_iou > best_iou_for_bbox[bbox_num]:\n","\t\t\t\t\t\t\t\tbest_anchor_for_bbox[bbox_num] = [jy, ix, anchor_ratio_idx, anchor_size_idx]\n","\t\t\t\t\t\t\t\tbest_iou_for_bbox[bbox_num] = curr_iou\n","\t\t\t\t\t\t\t\tbest_x_for_bbox[bbox_num,:] = [x1_anc, x2_anc, y1_anc, y2_anc]\n","\t\t\t\t\t\t\t\tbest_dx_for_bbox[bbox_num,:] = [tx, ty, tw, th]\n","\n","\t\t\t\t\t\t\t# we set the anchor to positive if the IOU is >0.7 (it does not matter if there was another better box, it just indicates overlap)\n","\t\t\t\t\t\t\tif curr_iou > C.rpn_max_overlap:\n","\t\t\t\t\t\t\t\tbbox_type = 'pos'\n","\t\t\t\t\t\t\t\tnum_anchors_for_bbox[bbox_num] += 1\n","\t\t\t\t\t\t\t\t# we update the regression layer target if this IOU is the best for the current (x,y) and anchor position\n","\t\t\t\t\t\t\t\tif curr_iou > best_iou_for_loc:\n","\t\t\t\t\t\t\t\t\tbest_iou_for_loc = curr_iou\n","\t\t\t\t\t\t\t\t\tbest_regr = (tx, ty, tw, th)\n","\n","\t\t\t\t\t\t\t# if the IOU is >0.3 and <0.7, it is ambiguous and no included in the objective\n","\t\t\t\t\t\t\tif C.rpn_min_overlap < curr_iou < C.rpn_max_overlap:\n","\t\t\t\t\t\t\t\t# gray zone between neg and pos\n","\t\t\t\t\t\t\t\tif bbox_type != 'pos':\n","\t\t\t\t\t\t\t\t\tbbox_type = 'neutral'\n","\n","\t\t\t\t\t# turn on or off outputs depending on IOUs\n","\t\t\t\t\tif bbox_type == 'neg':\n","\t\t\t\t\t\ty_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1\n","\t\t\t\t\t\ty_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0\n","\t\t\t\t\telif bbox_type == 'neutral':\n","\t\t\t\t\t\ty_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0\n","\t\t\t\t\t\ty_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0\n","\t\t\t\t\telif bbox_type == 'pos':\n","\t\t\t\t\t\ty_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1\n","\t\t\t\t\t\ty_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1\n","\t\t\t\t\t\tstart = 4 * (anchor_ratio_idx + n_anchratios * anchor_size_idx)\n","\t\t\t\t\t\ty_rpn_regr[jy, ix, start:start+4] = best_regr\n","\n","\t# we ensure that every bbox has at least one positive RPN region\n","\n","\tfor idx in range(num_anchors_for_bbox.shape[0]):\n","\t\tif num_anchors_for_bbox[idx] == 0:\n","\t\t\t# no box with an IOU greater than zero ...\n","\t\t\tif best_anchor_for_bbox[idx, 0] == -1:\n","\t\t\t\tcontinue\n","\t\t\ty_is_box_valid[\n","\t\t\t\tbest_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], best_anchor_for_bbox[idx,2] + n_anchratios *\n","\t\t\t\tbest_anchor_for_bbox[idx,3]] = 1\n","\t\t\ty_rpn_overlap[\n","\t\t\t\tbest_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], best_anchor_for_bbox[idx,2] + n_anchratios *\n","\t\t\t\tbest_anchor_for_bbox[idx,3]] = 1\n","\t\t\tstart = 4 * (best_anchor_for_bbox[idx,2] + n_anchratios * best_anchor_for_bbox[idx,3])\n","\t\t\ty_rpn_regr[\n","\t\t\t\tbest_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], start:start+4] = best_dx_for_bbox[idx, :]\n","\n","\ty_rpn_overlap = np.transpose(y_rpn_overlap, (2, 0, 1))\n","\ty_rpn_overlap = np.expand_dims(y_rpn_overlap, axis=0)\n","\n","\ty_is_box_valid = np.transpose(y_is_box_valid, (2, 0, 1))\n","\ty_is_box_valid = np.expand_dims(y_is_box_valid, axis=0)\n","\n","\ty_rpn_regr = np.transpose(y_rpn_regr, (2, 0, 1))\n","\ty_rpn_regr = np.expand_dims(y_rpn_regr, axis=0)\n","\n","\tpos_locs = np.where(np.logical_and(y_rpn_overlap[0, :, :, :] == 1, y_is_box_valid[0, :, :, :] == 1))\n","\tneg_locs = np.where(np.logical_and(y_rpn_overlap[0, :, :, :] == 0, y_is_box_valid[0, :, :, :] == 1))\n","\n","\tnum_pos = len(pos_locs[0])\n","\n","\t# one issue is that the RPN has many more negative than positive regions, so we turn off some of the negative\n","\t# regions. We also limit it to 256 regions.\n","\tnum_regions = 256\n","\n","\tif len(pos_locs[0]) > num_regions/2:\n","\t\tval_locs = random.sample(range(len(pos_locs[0])), len(pos_locs[0]) - num_regions/2)\n","\t\ty_is_box_valid[0, pos_locs[0][val_locs], pos_locs[1][val_locs], pos_locs[2][val_locs]] = 0\n","\t\tnum_pos = num_regions/2\n","\n","\tif len(neg_locs[0]) + num_pos > num_regions:\n","\t\tval_locs = random.sample(range(len(neg_locs[0])), len(neg_locs[0]) - num_pos)\n","\t\ty_is_box_valid[0, neg_locs[0][val_locs], neg_locs[1][val_locs], neg_locs[2][val_locs]] = 0\n","\n","\ty_rpn_cls = np.concatenate([y_is_box_valid, y_rpn_overlap], axis=1)\n","\ty_rpn_regr = np.concatenate([np.repeat(y_rpn_overlap, 4, axis=1), y_rpn_regr], axis=1)\n","\n","\treturn np.copy(y_rpn_cls), np.copy(y_rpn_regr), num_pos"],"execution_count":0,"outputs":[]},{"metadata":{"id":"3qGAalfJB8zz","colab_type":"text"},"cell_type":"markdown","source":["#### Get new image size and augment the image"]},{"metadata":{"id":"HKhSFbmB2RTo","colab_type":"code","colab":{}},"cell_type":"code","source":["def get_new_img_size(width, height, img_min_side=300):\n","\tif width <= height:\n","\t\tf = float(img_min_side) / width\n","\t\tresized_height = int(f * height)\n","\t\tresized_width = img_min_side\n","\telse:\n","\t\tf = float(img_min_side) / height\n","\t\tresized_width = int(f * width)\n","\t\tresized_height = img_min_side\n","\n","\treturn resized_width, resized_height\n","\n","def augment(img_data, config, augment=True):\n","\tassert 'filepath' in img_data\n","\tassert 'bboxes' in img_data\n","\tassert 'width' in img_data\n","\tassert 'height' in img_data\n","\n","\timg_data_aug = copy.deepcopy(img_data)\n","\n","\timg = cv2.imread(img_data_aug['filepath'])\n","\n","\tif augment:\n","\t\trows, cols = img.shape[:2]\n","\n","\t\tif config.use_horizontal_flips and np.random.randint(0, 2) == 0:\n","\t\t\timg = cv2.flip(img, 1)\n","\t\t\tfor bbox in img_data_aug['bboxes']:\n","\t\t\t\tx1 = bbox['x1']\n","\t\t\t\tx2 = bbox['x2']\n","\t\t\t\tbbox['x2'] = cols - x1\n","\t\t\t\tbbox['x1'] = cols - x2\n","\n","\t\tif config.use_vertical_flips and np.random.randint(0, 2) == 0:\n","\t\t\timg = cv2.flip(img, 0)\n","\t\t\tfor bbox in img_data_aug['bboxes']:\n","\t\t\t\ty1 = bbox['y1']\n","\t\t\t\ty2 = bbox['y2']\n","\t\t\t\tbbox['y2'] = rows - y1\n","\t\t\t\tbbox['y1'] = rows - y2\n","\n","\t\tif config.rot_90:\n","\t\t\tangle = np.random.choice([0,90,180,270],1)[0]\n","\t\t\tif angle == 270:\n","\t\t\t\timg = np.transpose(img, (1,0,2))\n","\t\t\t\timg = cv2.flip(img, 0)\n","\t\t\telif angle == 180:\n","\t\t\t\timg = cv2.flip(img, -1)\n","\t\t\telif angle == 90:\n","\t\t\t\timg = np.transpose(img, (1,0,2))\n","\t\t\t\timg = cv2.flip(img, 1)\n","\t\t\telif angle == 0:\n","\t\t\t\tpass\n","\n","\t\t\tfor bbox in img_data_aug['bboxes']:\n","\t\t\t\tx1 = bbox['x1']\n","\t\t\t\tx2 = bbox['x2']\n","\t\t\t\ty1 = bbox['y1']\n","\t\t\t\ty2 = bbox['y2']\n","\t\t\t\tif angle == 270:\n","\t\t\t\t\tbbox['x1'] = y1\n","\t\t\t\t\tbbox['x2'] = y2\n","\t\t\t\t\tbbox['y1'] = cols - x2\n","\t\t\t\t\tbbox['y2'] = cols - x1\n","\t\t\t\telif angle == 180:\n","\t\t\t\t\tbbox['x2'] = cols - x1\n","\t\t\t\t\tbbox['x1'] = cols - x2\n","\t\t\t\t\tbbox['y2'] = rows - y1\n","\t\t\t\t\tbbox['y1'] = rows - y2\n","\t\t\t\telif angle == 90:\n","\t\t\t\t\tbbox['x1'] = rows - y2\n","\t\t\t\t\tbbox['x2'] = rows - y1\n","\t\t\t\t\tbbox['y1'] = x1\n","\t\t\t\t\tbbox['y2'] = x2        \n","\t\t\t\telif angle == 0:\n","\t\t\t\t\tpass\n","\n","\timg_data_aug['width'] = img.shape[1]\n","\timg_data_aug['height'] = img.shape[0]\n","\treturn img_data_aug, img"],"execution_count":0,"outputs":[]},{"metadata":{"id":"0712o8CXkyh1","colab_type":"text"},"cell_type":"markdown","source":["#### Generate the ground_truth anchors"]},{"metadata":{"id":"TvsEv3RIk0cF","colab_type":"code","colab":{}},"cell_type":"code","source":["def get_anchor_gt(all_img_data, C, img_length_calc_function, mode='train'):\n","\t\"\"\" Yield the ground-truth anchors as Y (labels)\n","\t\t\n","\tArgs:\n","\t\tall_img_data: list(filepath, width, height, list(bboxes))\n","\t\tC: config\n","\t\timg_length_calc_function: function to calculate final layer's feature map (of base model) size according to input image size\n","\t\tmode: 'train' or 'test'; 'train' mode need augmentation\n","\n","\tReturns:\n","\t\tx_img: image data after resized and scaling (smallest size = 300px)\n","\t\tY: [y_rpn_cls, y_rpn_regr]\n","\t\timg_data_aug: augmented image data (original image with augmentation)\n","\t\tdebug_img: show image for debug\n","\t\tnum_pos: show number of positive anchors for debug\n","\t\"\"\"\n","\twhile True:\n","\n","\t\tfor img_data in all_img_data:\n","\t\t\ttry:\n","\n","\t\t\t\t# read in image, and optionally add augmentation\n","\n","\t\t\t\tif mode == 'train':\n","\t\t\t\t\timg_data_aug, x_img = augment(img_data, C, augment=True)\n","\t\t\t\telse:\n","\t\t\t\t\timg_data_aug, x_img = augment(img_data, C, augment=False)\n","\n","\t\t\t\t(width, height) = (img_data_aug['width'], img_data_aug['height'])\n","\t\t\t\t(rows, cols, _) = x_img.shape\n","\n","\t\t\t\tassert cols == width\n","\t\t\t\tassert rows == height\n","\n","\t\t\t\t# get image dimensions for resizing\n","\t\t\t\t(resized_width, resized_height) = get_new_img_size(width, height, C.im_size)\n","\n","\t\t\t\t# resize the image so that smalles side is length = 300px\n","\t\t\t\tx_img = cv2.resize(x_img, (resized_width, resized_height), interpolation=cv2.INTER_CUBIC)\n","\t\t\t\tdebug_img = x_img.copy()\n","\n","\t\t\t\ttry:\n","\t\t\t\t\ty_rpn_cls, y_rpn_regr, num_pos = calc_rpn(C, img_data_aug, width, height, resized_width, resized_height, img_length_calc_function)\n","\t\t\t\texcept:\n","\t\t\t\t\tcontinue\n","\n","\t\t\t\t# Zero-center by mean pixel, and preprocess image\n","\n","\t\t\t\tx_img = x_img[:,:, (2, 1, 0)]  # BGR -> RGB\n","\t\t\t\tx_img = x_img.astype(np.float32)\n","\t\t\t\tx_img[:, :, 0] -= C.img_channel_mean[0]\n","\t\t\t\tx_img[:, :, 1] -= C.img_channel_mean[1]\n","\t\t\t\tx_img[:, :, 2] -= C.img_channel_mean[2]\n","\t\t\t\tx_img /= C.img_scaling_factor\n","\n","\t\t\t\tx_img = np.transpose(x_img, (2, 0, 1))\n","\t\t\t\tx_img = np.expand_dims(x_img, axis=0)\n","\n","\t\t\t\ty_rpn_regr[:, y_rpn_regr.shape[1]//2:, :, :] *= C.std_scaling\n","\n","\t\t\t\tx_img = np.transpose(x_img, (0, 2, 3, 1))\n","\t\t\t\ty_rpn_cls = np.transpose(y_rpn_cls, (0, 2, 3, 1))\n","\t\t\t\ty_rpn_regr = np.transpose(y_rpn_regr, (0, 2, 3, 1))\n","\n","\t\t\t\tyield np.copy(x_img), [np.copy(y_rpn_cls), np.copy(y_rpn_regr)], img_data_aug, debug_img, num_pos\n","\n","\t\t\texcept Exception as e:\n","\t\t\t\tprint(e)\n","\t\t\t\tcontinue"],"execution_count":0,"outputs":[]},{"metadata":{"id":"FZAAMEH4uqu9","colab_type":"text"},"cell_type":"markdown","source":["#### Define loss functions for all four outputs"]},{"metadata":{"id":"CyLxnL4_uvmr","colab_type":"code","colab":{}},"cell_type":"code","source":["lambda_rpn_regr = 1.0\n","lambda_rpn_class = 1.0\n","\n","lambda_cls_regr = 1.0\n","lambda_cls_class = 1.0\n","\n","epsilon = 1e-4"],"execution_count":0,"outputs":[]},{"metadata":{"id":"tvGfH6m3yu0_","colab_type":"code","colab":{}},"cell_type":"code","source":["def rpn_loss_regr(num_anchors):\n","    \"\"\"Loss function for rpn regression\n","    Args:\n","        num_anchors: number of anchors (9 in here)\n","    Returns:\n","        Smooth L1 loss function \n","                           0.5*x*x (if x_abs < 1)\n","                           x_abx - 0.5 (otherwise)\n","    \"\"\"\n","    def rpn_loss_regr_fixed_num(y_true, y_pred):\n","\n","        # x is the difference between true value and predicted vaue\n","        x = y_true[:, :, :, 4 * num_anchors:] - y_pred\n","\n","        # absolute value of x\n","        x_abs = K.abs(x)\n","\n","        # If x_abs <= 1.0, x_bool = 1\n","        x_bool = K.cast(K.less_equal(x_abs, 1.0), tf.float32)\n","\n","        return lambda_rpn_regr * K.sum(\n","            y_true[:, :, :, :4 * num_anchors] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :, :, :4 * num_anchors])\n","\n","    return rpn_loss_regr_fixed_num\n","\n","\n","def rpn_loss_cls(num_anchors):\n","    \"\"\"Loss function for rpn classification\n","    Args:\n","        num_anchors: number of anchors (9 in here)\n","        y_true[:, :, :, :9]: [0,1,0,0,0,0,0,1,0] means only the second and the eighth box is valid which contains pos or neg anchor => isValid\n","        y_true[:, :, :, 9:]: [0,1,0,0,0,0,0,0,0] means the second box is pos and eighth box is negative\n","    Returns:\n","        lambda * sum((binary_crossentropy(isValid*y_pred,y_true))) / N\n","    \"\"\"\n","    def rpn_loss_cls_fixed_num(y_true, y_pred):\n","\n","            return lambda_rpn_class * K.sum(y_true[:, :, :, :num_anchors] * K.binary_crossentropy(y_pred[:, :, :, :], y_true[:, :, :, num_anchors:])) / K.sum(epsilon + y_true[:, :, :, :num_anchors])\n","\n","    return rpn_loss_cls_fixed_num\n","\n","\n","def class_loss_regr(num_classes):\n","    \"\"\"Loss function for rpn regression\n","    Args:\n","        num_anchors: number of anchors (9 in here)\n","    Returns:\n","        Smooth L1 loss function \n","                           0.5*x*x (if x_abs < 1)\n","                           x_abx - 0.5 (otherwise)\n","    \"\"\"\n","    def class_loss_regr_fixed_num(y_true, y_pred):\n","        x = y_true[:, :, 4*num_classes:] - y_pred\n","        x_abs = K.abs(x)\n","        x_bool = K.cast(K.less_equal(x_abs, 1.0), 'float32')\n","        return lambda_cls_regr * K.sum(y_true[:, :, :4*num_classes] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :, :4*num_classes])\n","    return class_loss_regr_fixed_num\n","\n","\n","def class_loss_cls(y_true, y_pred):\n","    return lambda_cls_class * K.mean(categorical_crossentropy(y_true[0, :, :], y_pred[0, :, :]))"],"execution_count":0,"outputs":[]},{"metadata":{"id":"5cX0N4VDl4zS","colab_type":"code","colab":{}},"cell_type":"code","source":["def non_max_suppression_fast(boxes, probs, overlap_thresh=0.9, max_boxes=300):\n","    # code used from here: http://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/\n","    # if there are no boxes, return an empty list\n","\n","    # Process explanation:\n","    #   Step 1: Sort the probs list\n","    #   Step 2: Find the larget prob 'Last' in the list and save it to the pick list\n","    #   Step 3: Calculate the IoU with 'Last' box and other boxes in the list. If the IoU is larger than overlap_threshold, delete the box from list\n","    #   Step 4: Repeat step 2 and step 3 until there is no item in the probs list \n","    if len(boxes) == 0:\n","        return []\n","\n","    # grab the coordinates of the bounding boxes\n","    x1 = boxes[:, 0]\n","    y1 = boxes[:, 1]\n","    x2 = boxes[:, 2]\n","    y2 = boxes[:, 3]\n","\n","    np.testing.assert_array_less(x1, x2)\n","    np.testing.assert_array_less(y1, y2)\n","\n","    # if the bounding boxes integers, convert them to floats --\n","    # this is important since we'll be doing a bunch of divisions\n","    if boxes.dtype.kind == \"i\":\n","        boxes = boxes.astype(\"float\")\n","\n","    # initialize the list of picked indexes\t\n","    pick = []\n","\n","    # calculate the areas\n","    area = (x2 - x1) * (y2 - y1)\n","\n","    # sort the bounding boxes \n","    idxs = np.argsort(probs)\n","\n","    # keep looping while some indexes still remain in the indexes\n","    # list\n","    while len(idxs) > 0:\n","        # grab the last index in the indexes list and add the\n","        # index value to the list of picked indexes\n","        last = len(idxs) - 1\n","        i = idxs[last]\n","        pick.append(i)\n","\n","        # find the intersection\n","\n","        xx1_int = np.maximum(x1[i], x1[idxs[:last]])\n","        yy1_int = np.maximum(y1[i], y1[idxs[:last]])\n","        xx2_int = np.minimum(x2[i], x2[idxs[:last]])\n","        yy2_int = np.minimum(y2[i], y2[idxs[:last]])\n","\n","        ww_int = np.maximum(0, xx2_int - xx1_int)\n","        hh_int = np.maximum(0, yy2_int - yy1_int)\n","\n","        area_int = ww_int * hh_int\n","\n","        # find the union\n","        area_union = area[i] + area[idxs[:last]] - area_int\n","\n","        # compute the ratio of overlap\n","        overlap = area_int/(area_union + 1e-6)\n","\n","        # delete all indexes from the index list that have\n","        idxs = np.delete(idxs, np.concatenate(([last],\n","            np.where(overlap > overlap_thresh)[0])))\n","\n","        if len(pick) >= max_boxes:\n","            break\n","\n","    # return only the bounding boxes that were picked using the integer data type\n","    boxes = boxes[pick].astype(\"int\")\n","    probs = probs[pick]\n","    return boxes, probs\n","\n","def apply_regr_np(X, T):\n","    \"\"\"Apply regression layer to all anchors in one feature map\n","\n","    Args:\n","        X: shape=(4, 18, 25) the current anchor type for all points in the feature map\n","        T: regression layer shape=(4, 18, 25)\n","\n","    Returns:\n","        X: regressed position and size for current anchor\n","    \"\"\"\n","    try:\n","        x = X[0, :, :]\n","        y = X[1, :, :]\n","        w = X[2, :, :]\n","        h = X[3, :, :]\n","\n","        tx = T[0, :, :]\n","        ty = T[1, :, :]\n","        tw = T[2, :, :]\n","        th = T[3, :, :]\n","\n","        cx = x + w/2.\n","        cy = y + h/2.\n","        cx1 = tx * w + cx\n","        cy1 = ty * h + cy\n","\n","        w1 = np.exp(tw.astype(np.float64)) * w\n","        h1 = np.exp(th.astype(np.float64)) * h\n","        x1 = cx1 - w1/2.\n","        y1 = cy1 - h1/2.\n","\n","        x1 = np.round(x1)\n","        y1 = np.round(y1)\n","        w1 = np.round(w1)\n","        h1 = np.round(h1)\n","        return np.stack([x1, y1, w1, h1])\n","    except Exception as e:\n","        print(e)\n","        return X\n","    \n","def apply_regr(x, y, w, h, tx, ty, tw, th):\n","    # Apply regression to x, y, w and h\n","    try:\n","        cx = x + w/2.\n","        cy = y + h/2.\n","        cx1 = tx * w + cx\n","        cy1 = ty * h + cy\n","        w1 = math.exp(tw) * w\n","        h1 = math.exp(th) * h\n","        x1 = cx1 - w1/2.\n","        y1 = cy1 - h1/2.\n","        x1 = int(round(x1))\n","        y1 = int(round(y1))\n","        w1 = int(round(w1))\n","        h1 = int(round(h1))\n","\n","        return x1, y1, w1, h1\n","\n","    except ValueError:\n","        return x, y, w, h\n","    except OverflowError:\n","        return x, y, w, h\n","    except Exception as e:\n","        print(e)\n","        return x, y, w, h\n","\n","def calc_iou(R, img_data, C, class_mapping):\n","    \"\"\"Converts from (x1,y1,x2,y2) to (x,y,w,h) format\n","\n","    Args:\n","        R: bboxes, probs\n","    \"\"\"\n","    bboxes = img_data['bboxes']\n","    (width, height) = (img_data['width'], img_data['height'])\n","    # get image dimensions for resizing\n","    (resized_width, resized_height) = get_new_img_size(width, height, C.im_size)\n","\n","    gta = np.zeros((len(bboxes), 4))\n","\n","    for bbox_num, bbox in enumerate(bboxes):\n","        # get the GT box coordinates, and resize to account for image resizing\n","        # gta[bbox_num, 0] = (40 * (600 / 800)) / 16 = int(round(1.875)) = 2 (x in feature map)\n","        gta[bbox_num, 0] = int(round(bbox['x1'] * (resized_width / float(width))/C.rpn_stride))\n","        gta[bbox_num, 1] = int(round(bbox['x2'] * (resized_width / float(width))/C.rpn_stride))\n","        gta[bbox_num, 2] = int(round(bbox['y1'] * (resized_height / float(height))/C.rpn_stride))\n","        gta[bbox_num, 3] = int(round(bbox['y2'] * (resized_height / float(height))/C.rpn_stride))\n","\n","    x_roi = []\n","    y_class_num = []\n","    y_class_regr_coords = []\n","    y_class_regr_label = []\n","    IoUs = [] # for debugging only\n","\n","    # R.shape[0]: number of bboxes (=300 from non_max_suppression)\n","    for ix in range(R.shape[0]):\n","        (x1, y1, x2, y2) = R[ix, :]\n","        x1 = int(round(x1))\n","        y1 = int(round(y1))\n","        x2 = int(round(x2))\n","        y2 = int(round(y2))\n","\n","        best_iou = 0.0\n","        best_bbox = -1\n","        # Iterate through all the ground-truth bboxes to calculate the iou\n","        for bbox_num in range(len(bboxes)):\n","            curr_iou = iou([gta[bbox_num, 0], gta[bbox_num, 2], gta[bbox_num, 1], gta[bbox_num, 3]], [x1, y1, x2, y2])\n","\n","            # Find out the corresponding ground-truth bbox_num with larget iou\n","            if curr_iou > best_iou:\n","                best_iou = curr_iou\n","                best_bbox = bbox_num\n","\n","        if best_iou < C.classifier_min_overlap:\n","                continue\n","        else:\n","            w = x2 - x1\n","            h = y2 - y1\n","            x_roi.append([x1, y1, w, h])\n","            IoUs.append(best_iou)\n","\n","            if C.classifier_min_overlap <= best_iou < C.classifier_max_overlap:\n","                # hard negative example\n","                cls_name = 'bg'\n","            elif C.classifier_max_overlap <= best_iou:\n","                cls_name = bboxes[best_bbox]['class']\n","                cxg = (gta[best_bbox, 0] + gta[best_bbox, 1]) / 2.0\n","                cyg = (gta[best_bbox, 2] + gta[best_bbox, 3]) / 2.0\n","\n","                cx = x1 + w / 2.0\n","                cy = y1 + h / 2.0\n","\n","                tx = (cxg - cx) / float(w)\n","                ty = (cyg - cy) / float(h)\n","                tw = np.log((gta[best_bbox, 1] - gta[best_bbox, 0]) / float(w))\n","                th = np.log((gta[best_bbox, 3] - gta[best_bbox, 2]) / float(h))\n","            else:\n","                print('roi = {}'.format(best_iou))\n","                raise RuntimeError\n","\n","        class_num = class_mapping[cls_name]\n","        class_label = len(class_mapping) * [0]\n","        class_label[class_num] = 1\n","        y_class_num.append(copy.deepcopy(class_label))\n","        coords = [0] * 4 * (len(class_mapping) - 1)\n","        labels = [0] * 4 * (len(class_mapping) - 1)\n","        if cls_name != 'bg':\n","            label_pos = 4 * class_num\n","            sx, sy, sw, sh = C.classifier_regr_std\n","            coords[label_pos:4+label_pos] = [sx*tx, sy*ty, sw*tw, sh*th]\n","            labels[label_pos:4+label_pos] = [1, 1, 1, 1]\n","            y_class_regr_coords.append(copy.deepcopy(coords))\n","            y_class_regr_label.append(copy.deepcopy(labels))\n","        else:\n","            y_class_regr_coords.append(copy.deepcopy(coords))\n","            y_class_regr_label.append(copy.deepcopy(labels))\n","\n","    if len(x_roi) == 0:\n","        return None, None, None, None\n","\n","    # bboxes that iou > C.classifier_min_overlap for all gt bboxes in 300 non_max_suppression bboxes\n","    X = np.array(x_roi)\n","    # one hot code for bboxes from above => x_roi (X)\n","    Y1 = np.array(y_class_num)\n","    # corresponding labels and corresponding gt bboxes\n","    Y2 = np.concatenate([np.array(y_class_regr_label),np.array(y_class_regr_coords)],axis=1)\n","\n","    return np.expand_dims(X, axis=0), np.expand_dims(Y1, axis=0), np.expand_dims(Y2, axis=0), IoUs"],"execution_count":0,"outputs":[]},{"metadata":{"id":"vT6X-fqJ1RSl","colab_type":"code","colab":{}},"cell_type":"code","source":["def rpn_to_roi(rpn_layer, regr_layer, C, dim_ordering, use_regr=True, max_boxes=300,overlap_thresh=0.9):\n","\t\"\"\"Convert rpn layer to roi bboxes\n","\n","\tArgs: (num_anchors = 9)\n","\t\trpn_layer: output layer for rpn classification \n","\t\t\tshape (1, feature_map.height, feature_map.width, num_anchors)\n","\t\t\tMight be (1, 18, 25, 18) if resized image is 400 width and 300\n","\t\tregr_layer: output layer for rpn regression\n","\t\t\tshape (1, feature_map.height, feature_map.width, num_anchors)\n","\t\t\tMight be (1, 18, 25, 72) if resized image is 400 width and 300\n","\t\tC: config\n","\t\tuse_regr: Wether to use bboxes regression in rpn\n","\t\tmax_boxes: max bboxes number for non-max-suppression (NMS)\n","\t\toverlap_thresh: If iou in NMS is larger than this threshold, drop the box\n","\n","\tReturns:\n","\t\tresult: boxes from non-max-suppression (shape=(300, 4))\n","\t\t\tboxes: coordinates for bboxes (on the feature map)\n","\t\"\"\"\n","\tregr_layer = regr_layer / C.std_scaling\n","\n","\tanchor_sizes = C.anchor_box_scales   # (3 in here)\n","\tanchor_ratios = C.anchor_box_ratios  # (3 in here)\n","\n","\tassert rpn_layer.shape[0] == 1\n","\n","\t(rows, cols) = rpn_layer.shape[1:3]\n","\n","\tcurr_layer = 0\n","\n","\t# A.shape = (4, feature_map.height, feature_map.width, num_anchors) \n","\t# Might be (4, 18, 25, 18) if resized image is 400 width and 300\n","\t# A is the coordinates for 9 anchors for every point in the feature map \n","\t# => all 18x25x9=4050 anchors cooridnates\n","\tA = np.zeros((4, rpn_layer.shape[1], rpn_layer.shape[2], rpn_layer.shape[3]))\n","\n","\tfor anchor_size in anchor_sizes:\n","\t\tfor anchor_ratio in anchor_ratios:\n","\t\t\t# anchor_x = (128 * 1) / 16 = 8  => width of current anchor\n","\t\t\t# anchor_y = (128 * 2) / 16 = 16 => height of current anchor\n","\t\t\tanchor_x = (anchor_size * anchor_ratio[0])/C.rpn_stride\n","\t\t\tanchor_y = (anchor_size * anchor_ratio[1])/C.rpn_stride\n","\t\t\t\n","\t\t\t# curr_layer: 0~8 (9 anchors)\n","\t\t\t# the Kth anchor of all position in the feature map (9th in total)\n","\t\t\tregr = regr_layer[0, :, :, 4 * curr_layer:4 * curr_layer + 4] # shape => (18, 25, 4)\n","\t\t\tregr = np.transpose(regr, (2, 0, 1)) # shape => (4, 18, 25)\n","\n","\t\t\t# Create 18x25 mesh grid\n","\t\t\t# For every point in x, there are all the y points and vice versa\n","\t\t\t# X.shape = (18, 25)\n","\t\t\t# Y.shape = (18, 25)\n","\t\t\tX, Y = np.meshgrid(np.arange(cols),np. arange(rows))\n","\n","\t\t\t# Calculate anchor position and size for each feature map point\n","\t\t\tA[0, :, :, curr_layer] = X - anchor_x/2 # Top left x coordinate\n","\t\t\tA[1, :, :, curr_layer] = Y - anchor_y/2 # Top left y coordinate\n","\t\t\tA[2, :, :, curr_layer] = anchor_x       # width of current anchor\n","\t\t\tA[3, :, :, curr_layer] = anchor_y       # height of current anchor\n","\n","\t\t\t# Apply regression to x, y, w and h if there is rpn regression layer\n","\t\t\tif use_regr:\n","\t\t\t\tA[:, :, :, curr_layer] = apply_regr_np(A[:, :, :, curr_layer], regr)\n","\n","\t\t\t# Avoid width and height exceeding 1\n","\t\t\tA[2, :, :, curr_layer] = np.maximum(1, A[2, :, :, curr_layer])\n","\t\t\tA[3, :, :, curr_layer] = np.maximum(1, A[3, :, :, curr_layer])\n","\n","\t\t\t# Convert (x, y , w, h) to (x1, y1, x2, y2)\n","\t\t\t# x1, y1 is top left coordinate\n","\t\t\t# x2, y2 is bottom right coordinate\n","\t\t\tA[2, :, :, curr_layer] += A[0, :, :, curr_layer]\n","\t\t\tA[3, :, :, curr_layer] += A[1, :, :, curr_layer]\n","\n","\t\t\t# Avoid bboxes drawn outside the feature map\n","\t\t\tA[0, :, :, curr_layer] = np.maximum(0, A[0, :, :, curr_layer])\n","\t\t\tA[1, :, :, curr_layer] = np.maximum(0, A[1, :, :, curr_layer])\n","\t\t\tA[2, :, :, curr_layer] = np.minimum(cols-1, A[2, :, :, curr_layer])\n","\t\t\tA[3, :, :, curr_layer] = np.minimum(rows-1, A[3, :, :, curr_layer])\n","\n","\t\t\tcurr_layer += 1\n","\n","\tall_boxes = np.reshape(A.transpose((0, 3, 1, 2)), (4, -1)).transpose((1, 0))  # shape=(4050, 4)\n","\tall_probs = rpn_layer.transpose((0, 3, 1, 2)).reshape((-1))                   # shape=(4050,)\n","\n","\tx1 = all_boxes[:, 0]\n","\ty1 = all_boxes[:, 1]\n","\tx2 = all_boxes[:, 2]\n","\ty2 = all_boxes[:, 3]\n","\n","\t# Find out the bboxes which is illegal and delete them from bboxes list\n","\tidxs = np.where((x1 - x2 >= 0) | (y1 - y2 >= 0))\n","\n","\tall_boxes = np.delete(all_boxes, idxs, 0)\n","\tall_probs = np.delete(all_probs, idxs, 0)\n","\n","\t# Apply non_max_suppression\n","\t# Only extract the bboxes. Don't need rpn probs in the later process\n","\tresult = non_max_suppression_fast(all_boxes, all_probs, overlap_thresh=overlap_thresh, max_boxes=max_boxes)[0]\n","\n","\treturn result"],"execution_count":0,"outputs":[]},{"metadata":{"colab_type":"text","id":"Kk14GTaNmqoo"},"cell_type":"markdown","source":["\n","\n","---\n","\n"]},{"metadata":{"id":"oNsi6HtyJPSb","colab_type":"text"},"cell_type":"markdown","source":["\n","\n","---\n","\n"]},{"metadata":{"id":"TVmMqXE5x70U","colab_type":"text"},"cell_type":"markdown","source":["### Start training"]},{"metadata":{"id":"C66bqGuOq7w6","colab_type":"code","colab":{}},"cell_type":"code","source":["base_path = 'drive/My Drive/AI/Faster_RCNN'\n","\n","train_path =  'drive/My Drive/AI/Dataset/Open Images Dataset v4 (Bounding Boxes)/person_car_phone_train_annotation.txt' # Training data (annotation file)\n","\n","num_rois = 4 # Number of RoIs to process at once.\n","\n","# Augmentation flag\n","horizontal_flips = True # Augment with horizontal flips in training. \n","vertical_flips = True   # Augment with vertical flips in training. \n","rot_90 = True           # Augment with 90 degree rotations in training. \n","\n","output_weight_path = os.path.join(base_path, 'model/model_frcnn_vgg.hdf5')\n","\n","record_path = os.path.join(base_path, 'model/record.csv') # Record data (used to save the losses, classification accuracy and mean average precision)\n","\n","base_weight_path = os.path.join(base_path, 'model/vgg16_weights_tf_dim_ordering_tf_kernels.h5')\n","\n","config_output_filename = os.path.join(base_path, 'model_vgg_config.pickle')"],"execution_count":0,"outputs":[]},{"metadata":{"id":"J3oAmbbEutH0","colab_type":"code","colab":{}},"cell_type":"code","source":["# Create the config\n","C = Config()\n","\n","C.use_horizontal_flips = horizontal_flips\n","C.use_vertical_flips = vertical_flips\n","C.rot_90 = rot_90\n","\n","C.record_path = record_path\n","C.model_path = output_weight_path\n","C.num_rois = num_rois\n","\n","C.base_net_weights = base_weight_path"],"execution_count":0,"outputs":[]},{"metadata":{"id":"yiEaAmb-x-so","colab_type":"code","outputId":"eb280e97-7692-470f-fbf2-e0f1cf29ef31","executionInfo":{"status":"ok","timestamp":1542545119215,"user_tz":-480,"elapsed":36108,"user":{"displayName":"Yinghan Xu","photoUrl":"https://lh3.googleusercontent.com/-ZH_vkc0a6g0/AAAAAAAAAAI/AAAAAAAAAAc/x8TPjkqmxys/s64/photo.jpg","userId":"13488364327507272606"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"cell_type":"code","source":["#--------------------------------------------------------#\n","# This step will spend some time to load the data        #\n","#--------------------------------------------------------#\n","st = time.time()\n","train_imgs, classes_count, class_mapping = get_data(train_path)\n","print()\n","print('Spend %0.2f mins to load the data' % ((time.time()-st)/60) )"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Parsing annotation files\n","idx=7236\n","Spend 0.59 mins to load the data\n"],"name":"stdout"}]},{"metadata":{"id":"x-nuSdC56GsK","colab_type":"code","outputId":"7b7d16df-5aa4-4b7a-decf-b0b24eec0758","executionInfo":{"status":"error","timestamp":1542596406207,"user_tz":-480,"elapsed":1523,"user":{"displayName":"Yinghan Xu","photoUrl":"https://lh3.googleusercontent.com/-ZH_vkc0a6g0/AAAAAAAAAAI/AAAAAAAAAAc/x8TPjkqmxys/s64/photo.jpg","userId":"13488364327507272606"}},"colab":{"base_uri":"https://localhost:8080/","height":232}},"cell_type":"code","source":["if 'bg' not in classes_count:\n","\tclasses_count['bg'] = 0\n","\tclass_mapping['bg'] = len(class_mapping)\n","# e.g.\n","#    classes_count: {'Car': 2383, 'Mobile phone': 1108, 'Person': 3745, 'bg': 0}\n","#    class_mapping: {'Person': 0, 'Car': 1, 'Mobile phone': 2, 'bg': 3}\n","C.class_mapping = class_mapping\n","\n","print('Training images per class:')\n","pprint.pprint(classes_count)\n","print('Num classes (including bg) = {}'.format(len(classes_count)))\n","print(class_mapping)\n","\n","# Save the configuration\n","with open(config_output_filename, 'wb') as config_f:\n","\tpickle.dump(C,config_f)\n","\tprint('Config has been written to {}, and can be loaded when testing to ensure correct results'.format(config_output_filename))\n"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-d4748950189a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m'bg'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclasses_count\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m         \u001b[0mclasses_count\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'bg'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0mclass_mapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'bg'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_mapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# e.g.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#    classes_count: {'Car': 2383, 'Mobile phone': 1108, 'Person': 3745, 'bg': 0}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'classes_count' is not defined"]}]},{"metadata":{"id":"LFlq36Sx4F4O","colab_type":"code","outputId":"e2576f93-290b-48ff-ed8c-fda147fc53df","executionInfo":{"status":"ok","timestamp":1542545124369,"user_tz":-480,"elapsed":610,"user":{"displayName":"Yinghan Xu","photoUrl":"https://lh3.googleusercontent.com/-ZH_vkc0a6g0/AAAAAAAAAAI/AAAAAAAAAAc/x8TPjkqmxys/s64/photo.jpg","userId":"13488364327507272606"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["# Shuffle the images with seed\n","random.seed(1)\n","random.shuffle(train_imgs)\n","\n","print('Num train samples (images) {}'.format(len(train_imgs)))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Num train samples (images) 2396\n"],"name":"stdout"}]},{"metadata":{"id":"GXIV1uXyBo3v","colab_type":"code","colab":{}},"cell_type":"code","source":["# Get train data generator which generate X, Y, image_data\n","data_gen_train = get_anchor_gt(train_imgs, C, get_img_output_length, mode='train')"],"execution_count":0,"outputs":[]},{"metadata":{"id":"y_yM5jkKqM1G","colab_type":"text"},"cell_type":"markdown","source":["#### Explore 'data_gen_train'\n","\n","data_gen_train is an **generator**, so we get the data by calling **next(data_gen_train)**"]},{"metadata":{"id":"nIDnio1UlRHi","colab_type":"code","colab":{}},"cell_type":"code","source":["X, Y, image_data, debug_img, debug_num_pos = next(data_gen_train)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"dZXoJ2e3l2Ey","colab_type":"code","outputId":"eb1a0f85-93f4-46cb-9046-77afd48b59b5","executionInfo":{"status":"ok","timestamp":1542544779625,"user_tz":-480,"elapsed":1611,"user":{"displayName":"Yinghan Xu","photoUrl":"https://lh3.googleusercontent.com/-ZH_vkc0a6g0/AAAAAAAAAAI/AAAAAAAAAAc/x8TPjkqmxys/s64/photo.jpg","userId":"13488364327507272606"}},"colab":{"base_uri":"https://localhost:8080/","height":848}},"cell_type":"code","source":["print('Original image: height=%d width=%d'%(image_data['height'], image_data['width']))\n","print('Resized image:  height=%d width=%d C.im_size=%d'%(X.shape[1], X.shape[2], C.im_size))\n","print('Feature map size: height=%d width=%d C.rpn_stride=%d'%(Y[0].shape[1], Y[0].shape[2], C.rpn_stride))\n","print(X.shape)\n","print(str(len(Y))+\" includes 'y_rpn_cls' and 'y_rpn_regr'\")\n","print('Shape of y_rpn_cls {}'.format(Y[0].shape))\n","print('Shape of y_rpn_regr {}'.format(Y[1].shape))\n","print(image_data)\n","\n","print('Number of positive anchors for this image: %d' % (debug_num_pos))\n","if debug_num_pos==0:\n","    gt_x1, gt_x2 = image_data['bboxes'][0]['x1']*(X.shape[2]/image_data['height']), image_data['bboxes'][0]['x2']*(X.shape[2]/image_data['height'])\n","    gt_y1, gt_y2 = image_data['bboxes'][0]['y1']*(X.shape[1]/image_data['width']), image_data['bboxes'][0]['y2']*(X.shape[1]/image_data['width'])\n","    gt_x1, gt_y1, gt_x2, gt_y2 = int(gt_x1), int(gt_y1), int(gt_x2), int(gt_y2)\n","\n","    img = debug_img.copy()\n","    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n","    color = (0, 255, 0)\n","    cv2.putText(img, 'gt bbox', (gt_x1, gt_y1-5), cv2.FONT_HERSHEY_DUPLEX, 0.7, color, 1)\n","    cv2.rectangle(img, (gt_x1, gt_y1), (gt_x2, gt_y2), color, 2)\n","    cv2.circle(img, (int((gt_x1+gt_x2)/2), int((gt_y1+gt_y2)/2)), 3, color, -1)\n","\n","    plt.grid()\n","    plt.imshow(img)\n","    plt.show()\n","else:\n","    cls = Y[0][0]\n","    pos_cls = np.where(cls==1)\n","    print(pos_cls)\n","    regr = Y[1][0]\n","    pos_regr = np.where(regr==1)\n","    print(pos_regr)\n","    print('y_rpn_cls for possible pos anchor: {}'.format(cls[pos_cls[0][0],pos_cls[1][0],:]))\n","    print('y_rpn_regr for positive anchor: {}'.format(regr[pos_regr[0][0],pos_regr[1][0],:]))\n","\n","    gt_x1, gt_x2 = image_data['bboxes'][0]['x1']*(X.shape[2]/image_data['width']), image_data['bboxes'][0]['x2']*(X.shape[2]/image_data['width'])\n","    gt_y1, gt_y2 = image_data['bboxes'][0]['y1']*(X.shape[1]/image_data['height']), image_data['bboxes'][0]['y2']*(X.shape[1]/image_data['height'])\n","    gt_x1, gt_y1, gt_x2, gt_y2 = int(gt_x1), int(gt_y1), int(gt_x2), int(gt_y2)\n","\n","    img = debug_img.copy()\n","    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n","    color = (0, 255, 0)\n","    #   cv2.putText(img, 'gt bbox', (gt_x1, gt_y1-5), cv2.FONT_HERSHEY_DUPLEX, 0.7, color, 1)\n","    cv2.rectangle(img, (gt_x1, gt_y1), (gt_x2, gt_y2), color, 2)\n","    cv2.circle(img, (int((gt_x1+gt_x2)/2), int((gt_y1+gt_y2)/2)), 3, color, -1)\n","\n","    # Add text\n","    textLabel = 'gt bbox'\n","    (retval,baseLine) = cv2.getTextSize(textLabel,cv2.FONT_HERSHEY_COMPLEX,0.5,1)\n","    textOrg = (gt_x1, gt_y1+5)\n","    cv2.rectangle(img, (textOrg[0] - 5, textOrg[1]+baseLine - 5), (textOrg[0]+retval[0] + 5, textOrg[1]-retval[1] - 5), (0, 0, 0), 2)\n","    cv2.rectangle(img, (textOrg[0] - 5,textOrg[1]+baseLine - 5), (textOrg[0]+retval[0] + 5, textOrg[1]-retval[1] - 5), (255, 255, 255), -1)\n","    cv2.putText(img, textLabel, textOrg, cv2.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 0), 1)\n","\n","    # Draw positive anchors according to the y_rpn_regr\n","    for i in range(debug_num_pos):\n","\n","        color = (100+i*(155/4), 0, 100+i*(155/4))\n","\n","        idx = pos_regr[2][i*4]/4\n","        anchor_size = C.anchor_box_scales[int(idx/3)]\n","        anchor_ratio = C.anchor_box_ratios[2-int((idx+1)%3)]\n","\n","        center = (pos_regr[1][i*4]*C.rpn_stride, pos_regr[0][i*4]*C.rpn_stride)\n","        print('Center position of positive anchor: ', center)\n","        cv2.circle(img, center, 3, color, -1)\n","        anc_w, anc_h = anchor_size*anchor_ratio[0], anchor_size*anchor_ratio[1]\n","        cv2.rectangle(img, (center[0]-int(anc_w/2), center[1]-int(anc_h/2)), (center[0]+int(anc_w/2), center[1]+int(anc_h/2)), color, 2)\n","#         cv2.putText(img, 'pos anchor bbox '+str(i+1), (center[0]-int(anc_w/2), center[1]-int(anc_h/2)-5), cv2.FONT_HERSHEY_DUPLEX, 0.5, color, 1)\n","\n","print('Green bboxes is ground-truth bbox. Others are positive anchors')\n","plt.figure(figsize=(8,8))\n","plt.grid()\n","plt.imshow(img)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Original image: height=768 width=1024\n","Resized image:  height=300 width=400 C.im_size=300\n","Feature map size: height=18 width=25 C.rpn_stride=16\n","(1, 300, 400, 3)\n","2 includes 'y_rpn_cls' and 'y_rpn_regr'\n","Shape of y_rpn_cls (1, 18, 25, 18)\n","Shape of y_rpn_regr (1, 18, 25, 72)\n","{'filepath': 'drive/My Drive/AI/Dataset/Open Images Dataset v4 (Bounding Boxes)/train/1cb71505057b99dc.jpg', 'width': 1024, 'height': 768, 'bboxes': [{'class': 'Car', 'x1': 80, 'x2': 781, 'y1': 292, 'y2': 460}]}\n","Number of positive anchors for this image: 1\n","(array([ 9,  9, 15]), array([12, 12, 16]), array([ 5, 14,  5]))\n","(array([9, 9, 9, 9]), array([12, 12, 12, 12]), array([20, 21, 22, 23]))\n","y_rpn_cls for possible pos anchor: [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n","y_rpn_regr for positive anchor: [ 0.          0.          0.          0.          0.          0.\n","  0.          0.          0.          0.          0.          0.\n","  0.          0.          0.          0.          0.          0.\n","  0.          0.          1.          1.          1.          1.\n","  0.          0.          0.          0.          0.          0.\n","  0.          0.          0.          0.          0.          0.\n","  0.          0.          0.          0.          0.          0.\n","  0.          0.          0.          0.          0.          0.\n","  0.          0.          0.          0.          0.          0.\n","  0.          0.         -0.7034815  -0.22649515  1.65558708 -1.28599977\n","  0.          0.          0.          0.          0.          0.\n","  0.          0.          0.          0.          0.          0.        ]\n","Center position of positive anchor:  (192, 144)\n","Green bboxes is ground-truth bbox. Others are positive anchors\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAeQAAAFxCAYAAACiBdsJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7p0P1n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htSAgamiIWw+jb42yXKQrLrlzWytPTlafWcAb7GPiEJ7d7Z3sEW0/hFBv57zH7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6nbRvabser2zv61HHfd7h+MEPlhT14FOFB6fsQ0KK0yp78Bx7GYZgvGEge/HsRWoySr1fDhEkC\nlQixJ7QhbwQTXzSiqPzSmJJUNobMcnMIqiCH4QxksR/n3xy//jsf/7+0A1SGKJS+rw7/yTnUFqsF\nOToQxjAzki3QAyu5KMFolC0pHnjBY3po5IuopPAu0LiONnp2KgIuf15Sjn7v6jEfnF9xeXTC+dER\n88kEpQI+BrqMxb1rNrzarHi5es3HmxvebLekDi7rJR+ePuLZ+TkAl4sFZ7M5EyNWraZvaYPjvtvx\naiMunI9vr3m+umHT9IQ2cTk756PjD3n/7JxHxwvOZpntb43YXIOj8y23+zWvVrc8X694vpIT8pvt\nDpKUup9dPuXJ7Iiny2Pm0xpTCokNwKuCtYuQ1sRk8NESVUnQB5qhUoO58G1c6+BHfnhXaK1GyM9Q\npQmIP538NYURitxDRsEXx/ddkH/v7/29/Oqv/up3/f1f/+t//bv+7qd+6qf4qZ/6qe/3luN48byl\nriqqaopWFWWOQ5yeFzx9Uo6hAIXJEAitGVI3QqbspBQzTckRY2Tb3uE6N9b3nXcEJ4rpb//9v/MD\n/2zvxm9vNLcvuR0CDrJjVkUtnvLeEYNHq5TjT/KUlxgBEhZhg1tbjslaIH7MYjZjUk+Y1NMcqVlS\n11Omy+xntJaqnkkKThbzFfVMTopavbUxC16ISW0Y4i8j+/1+fBi9c/RNS98Lh7tr2xyqEfCufbDh\nC1lwmBOakoPkR1AFDKSuQIpD3OHh34YKEQDKZVKUxMUFL97HVXM45YQo9CClMqnbC4JVDtaH1wnt\nzhL9nuEIO5RXtdbYUgAPSmmKoiJ5S2E1ShmmE4kdHa6WUYK3VQZsAIsw1+ktyRWEXBJNOhH1XuLo\nUiKV1UjAUkpK/NNJQVlATJquizRNxJTQ9o626emyGMi5SNO1dLHEaSPhDCS0tmMZMKWIysz7h6dK\nYOTBw9C/OxCUYoKU8uQ5fk2GOaiUhZvyd0pJqXJYeIt8IiclSqVG8Ia0yQbKmKY0hpBjWh+O3u3H\n9KKgDCSF5qsPDCP+E9ngzmYzSaKqxYUylAyTMUSlSVoRO6kmYhWl0QJMAilRxyRtIm0JwaFDDygK\na3h8KolJT86uuDw+4Wx5zHxSS3SpgsY13OWT6PX9is9Wr/j89g0vdve4PjJXc37k5AnPTi+4WObg\niPmMxWSGziS8fbdn0+54efuGFxsBfnx6e8tt29C1kcfTE75x8h5PlhdcnshBMQAAIABJREFUzo44\nqUqmubdN9LQ+sXMN6+2K16trnr+55sV2y5udHNSULTmfHfP++ROu5mdcLhYsJhMmxpCwYzXNhw6F\noSgmTOfHHB2VGHOF9xEyt1y49BatI0oP3HrpK1trx42oEOqG+0A/iJr1WD2ES8jrNPq7Yhkfjq+V\n1PX64+9kApUhoQ5ZtFGhTC8JNinlY35GCuQdrU7mwa7ZUE9qSltgKkNVVsyGPOTZgrIox2i0d+OH\nOxbz96j6gZMsrYi2aSiQtn+serSJY69wwBumJL0Vq4Rx3PuGYA0uT+RKG9jvWeVF1TlHSnLq63LV\nJBJHxfohrlAW/6FUCw9OHUpxVArDdjKbUlYV03pIvKmZLi6pq5qrxYLq/FxU+NZiJ0sYlauC5ZTs\nYg1ajVGOQ3kv9JKs1XvPPsNu+r5HcrWl8iPXQnrmQtSKmaIkVYDDIpQYSHQJIEaJicvQljRSzhRK\nRRLHkBef3nUCb0uJlG0iSilC6um7BtXL13E7xB9m7nnmLYcYICp0HFLS+MKJVROSEzWv0bmkW8q1\nLg+92qKYSemvSNgCZstIHQSZ6EYrYse+adj1DU2/Z79vRpviiBpVEbQfaW8DfTKmSKnbUZwz4EgT\niRDl3gu5uvZwMYyk0eaocuEixoyMzZNv3/cYJarZrtsRgyN5h4tqrITMZjMW8yMmkwkuvn38rata\nQiWAIttiUjrkHo9pV3kkpWido6wroAImGLukLObMJkeUlVSjqrqinE0pjKGaikbCWCtkqQF5HwUg\n5LqeJuzYNPds9nv2fWQynXF+IovocjbjYnHCcjZFGxEK7rrAdrdjleMXX2+ueX77mhebNU1nWJYz\nPrp4xLOTU66mC87q4b0meHpSjHRdy6a95+X9LS82b/hnt68AuN91hDZyMVny4fklT44XXC4nXCzn\nGBUzYhhc6tm7jvX9Hdf3a37z9pbPt3tuNy3Byf3/wekpv+fiKaeTOU8Xc45mU6kmlRbt3Ci0DD6h\nvMK4gsvJObOrRDgXjv+wRTN5oVUqPciCL0TEaTUm256sqQ953abILRTJYh9ayAkJYdF89WIMX/OC\nrKeSiepjJEVJQ5J/UCQsSRmhUCEeXaUVAywqDNnGJDSK3XZLSgETyRO1TPS2sJC+egf6bvzujvtO\nWg0AyiiU0VTzHNOYEkSNxo4dGMnDHuwBslMfBBLSohzg7J7Uh2xHy7nV1uS0pMv8XiafqnNogBJ8\nndZKuMrje4kSO6ZIDKKk3TQN/v7+gf8zsu3+/qiIDkGEL67v0bp46zT2EJ2pqDF6SlXXlDlAwxrD\nZDKhKEuqyZzZbE5dV0wmNct6SrUYwtxPsy1PTvhS1pbozWHdGPyoslENuJDoXJ/52kEC7yH76RPt\n3uC6buSmD5a8Q0k4knKc5lCO68OdiL+GU7kPYymOcGioDtGKh0QbcOkwCYUYcCmRXCS1ObwkMwJk\nQ5JbBTHKI6qgy0EhQwiEYsm0uGJ5lm2ImLfaHXUpm6shRWrgyrf99kBSipF9s8+Rqo7N/Z3ko3s3\nxlUOEaAq7THWyQnHKImgNHYUJnq/wxYFVVkwLSyVNSKuUuYgQCRQ1zXGmBFJO4zFdMbQGGlcT/Rx\n7EkPTPG3TvtK5erH4ZSftMYWRqpG2bppywnWlNjSEJPK1K+cp55/rq7r6LuettnTui33+w0uRBbL\nc46nS07zfXg8mzGfVqgQ8SnSth33zZbX69e8yASuT1bXXO/u6VxiUUx57+wR718+4ur4lIvZnFne\n2A5JZX3v2Ow3fH53w/O7Nzy/fcNdRps6B4tqweOTCx4fn3N1esZRNaPKmzgXBrRsz932jlera0me\nWq1Y7zoIivNsjXp6Kifr08Wco8WM0hSQPG3fkpynz6EXLkpk7rSeMC1rJhOPtQWlLZnN5ToYa6jK\nGmsU2gqS12Rsps6fO4jt1liLNoaqyGlvWmHMQbmUyFVAdXh+vmx8X1LXu/FuvBvvxrvxbrwbP/zx\ntZ6Q//3/8A/n0liSbNch5tB59r3QrXrX0zU9veto2oboBoqKpO6ELOJyzhF9kMBvH0Z/4fDqUX7+\nbvxQx+vVG7QZjpk5UC5lD2wWNZhkRkW9tRYf3Kgw7eIhUCJEn+1uoK0iBU/SEbQUgwOazvcjfzoX\nRUT0kkVOGgEDpAe+2sKWue8YcAZUWZJSxJQlWh3Kr6abUhaiak4qSekvwnI6IYYDiXmwLQ2nxYdC\nMSDHeW5wXWR7+zmf54QjpRS968dTmsLkkrUbT00PQwXgkLw0hHokK+lZdV1TliXTmVjAppMpRSG7\n/el0wnJWMZ3OqKojea0V4lJpS0pboK2BXFLba0dIkfDgZOXzM+ZdwHVD4pX03rt8KvfB43JFwees\n4ZRPfMofTsgpyddI+zxiclJSiGH01YYo0atJ+XxFcwKTjwehX0woZ/F9wqV4AC4oxbofOsYC8nHe\n5ehKw9Hy9K3rK1+icgyrorSiqNfGoAs7ziUA2+1K7Gl9gBSxOqHRFOqgwDUWykp6jzN/oD4B9CHi\ns9fdOS+/74Oe4kFpnVsUSix/5IqgRgI8TAm2UmOGsS0L6W0WJfVkqNgMdqfD+wunvqPrdux29yht\nqKspdVkxO5J7fz6ZYhU439O5nrv9hrv7az5dv+Y7W+n7fnK/JvYwMTO+cf6ID66ecDGdcTqdsZhM\nUfnnD12Lcx2b/Y7P7iQs4pP1iuvNhraV63A8Peb9s6ciJFuccVzNmExKYpIWRp+Dd1b7LderGz65\nv+fz9S132y3BK04XZzw7ewTA++cXXMwWHE2nWJWIviMQaNqG3nVj6/L86ozTox/h/OSCk5M5REVZ\naGlH5K5ByhoD7+XPMSZUrtiGmN4qRw8tHdf3OUs7SatnSHwj0fcNIcTMMDjmy8bXuiD/H//7/0ZV\niWKzrGtMztGtihIqKckc1zXVbEpRHqOtYVplKICtpHludLYGZC8dOdUkHBb3IQHmf/mV3/3f4cvM\n/D+Mr/2dfJ9/meMn/+3/gLYTO0NKkaZt2G33kmDUt9A7FH70BJtc3olZqJSy1SmlQZ08LO5IfGBK\nRLKlwyf5zIMg/5KSV5eFlJSd9ygvofbG2rFQJJaVAm0MZTnYccQnGB+8pozHJCdq2JQBGkop1k2L\nf5C1a3TK1piEMRFthsU/q6d1SWkUaE2InpmRxCLJCz7Yo9QQw5e9O8Mi8ZYNhpQB9ylbWzzRSymu\n3W7ZZ6FM3/f40NGHW2JMFFZAJYMgebBjSN6upqpLRIan0OaEoqwOAA5bUZYF0/mUaT1jOp2JuKiu\nmU+nTLKApyjLkYRXVRUueKyW0l4XDtjVlBJt04hFzTkB+WR4ymBzGzKtnY8Cjhk2ACGMLZE+BPHY\npgf9YcQHfGbXB2PKULqWui8qGdlwhMMGwA9le6+I3iIhWRHv9/gUiWmIckyEoEleBGZ9LwcCGzwp\nx2VoA7bQ2YeeeAiXPT4+x1aSWFf3e5r9nhTeFvk9XJBN8miGoBsPKaJSpG3uKazJSmAovCdWFUlX\noErR5eSwBJWneVsVWG1ZTiucm1A9fiKJW4CtDEHLxiqFQNe1NL7jrtny5v6W6801n9y+5vN9jmh0\nsCzmfHTxAR+cnPB4ueTq6JhFVaNJ+Pzzt03Ltt3w+fqO56sbPrm74dXuji5GzqdSZn7v6j0+OH3M\no8UxZ7Ml07oE1eJDFJ/xTtTYN9s1L27f8J27FdvdnuThZHrEh2ePeLI8A+B8NudoNqE0Eo/b+5Y2\n9Kz3W3b7HbdvRI29vVvjeo9JGlykGm20B7pWiBIfKhoPac/E/ADJdu9g0RsElTEEtDFZyHjoR/NA\n5wCKb/3KL/Jl42tdkP/Jt/9e7o0MkWwDnUiLEIBDf2ogdhVGJgnhPR96WcYY0Ao1kUmhzJ7A4+Nj\nqrqmKr8YBv9u/DDGj37zx7A5gkZoWDrbcYQR2/VrXLNhtx8AFkJHaptWhFAK2raja1uWc4sfqibB\nY/JC7IOXhSovTI7BVnQ49eih16MOcYwDvEGRvawhooITVXCMxJ6x3yYo1E8FSpPpWCrnUFPV4ylN\nKUVVlgyRbDrV6DTNwfX5oiTZbEietcZoJ6dRY1D48cEO+XUaEV4Nas2yKN5yxuhsr8AYLFBqKApR\nbw+noZB7yPv9hVjA+o6yUvl0bSknh5jGoiiZ1FX+nRQVYkUcxGau7wltw3p7y10WTfVDdSoL2MjX\nNCCRmBHpXdp8km+DbLbr/GzWdY0tSyZVxXy+oCzFu/sQeVuWwmOuF1PK6lgqAJOJpKcB2lpUXcsm\nQit8CAe1eavGiptzjvv7+3ETE4Nk1Yp1Loyv6bqW4HvJJXc9Lm8M0Xps7mmd8D5kjGIWvcVEjP2o\nlrdGEZNDW0XftTxMFGt7T5P75F3Tjza1h0P0Afn+0nmTlBKaQNNscc5ja8V2tUbpz+RnQINJKJUo\nraWwpQi9igPL2liNVgZra7SynJ9fsFwuWSxnKFvhsgLZeU/bN9x1e15vbnhx8znfWX/O9d0W5+VC\nHBcL3j9+woenVzxdLmUhrKdoEn10o7Xrzt1zvbvjxeaGz7YrbjcbnEsspkuenYr2473TSx4fH3M+\nW7Kc14TU4qKTnnF7z5t7WZA/uXvDZ3e33G33EDQXi2O+efmUD44fcbaUbc/5fEFdaZLPmch9w6rd\nst5suN9uadssZiwslycnzMoJtamoCzWKFMeKlRrWJTXej2VRZgqaQWeL5vD/pfLTo9Rhgz9srEKI\nY7749xpf64Jspj57txTed6OC2kfo3AMhSUwiPInhcAMnRjGQTJry57iPbAbzPPC6rr7LEvFu/PDG\nt//RPxxvQp9taT4vDlKWbEkpjApYlFClqrKiqCwmKSZ2zvx4yWQyoSwGlGLB7EhyXo22HB8fkZSc\nWt3IhhWB2G63zf72wL7fS3k1+BF0EfJprHc9JsbsbZZ84noocyqNU/cYo6mKkrYT+IbvnZyA42Ei\nPwBtIpGISi1eHWACwfsREBGTGTF6xgyxnoPH2EIu8T8Ucfni7aBAa+248eizDeqLJ+nhd0BLXJ8p\nrKjB1eBMkEUtRuh7OfnJYhZxStoGJvtSSQZtE0WZFzQFxYNy+rCIaq0p6mr0aaqcvx1jZJ7JesIE\njjTNnhgDMfa8ef0cHwVdGfxBTR6iI9GTEPX5ELJwOE0KYlPALbJpqspaXBX6hEmupk3rGlsWo4p+\nPjuiqmvmiwWL7HEty2Pq6YS6KilrQXEaY/HRiyp9AIPknz+h2e1aOjcs7O5BprgjBE/X9dze3rLn\nH42fiUY/EJuFL/nM1NtWuBgBQXfGKCrgojIUKqFCSxrwrNHhUov3ewwlA8xCxfFYJiXYaNC6pizn\nvPfsQy4fXXLcLyl2Fj+0axT4FLnerXi5vuHF6obPfYOLcKJF1f108YhvXDzh8uSYi/mc2UROxpFE\nFzx3e9mEvLy/5fn6hpebW27u79k1HUeLE56eXfJBXpAvFwvOpzOmlSHQ0YWGNvZs9lterd/w2VoI\nkC/ubrjptvhecbpY8P75Ix4dXXA2W3AxlxJwZRUh9KCS2LT2a17fr1hvNrS7lr6R61Vpi75P9Lql\n1EY2PA/aQsNnIVamEmi+q8Ux2hofRP36nB0v60988NmmrKaXz/Fn/uOf5MvG17ogM9nio/SconqA\njVOJIlSZwhQJQR6IqCL9g1KbkHk0MZcBdDLYKH7CsdIZDVoL6u9fZHwVTORAbFFf+trfzsL/xe/1\nvd7jq9B6v533/aoy+MNs5H/hYUtSkpteW9DKUtsZjGW/KSnFcRcafEAXMtl2PjKbzIRKlCK+7XGZ\nUet9QD+XII7ko5SDNBj0WD4eJh7Z6cpCpI2oJYuiGBfO0lrmR6cYa6lnC+qqEh5yUYz2Bq0Ntqwz\n4U1ypBXipw5dM/aQu65jt9tly05P53f0fkvbdoffcSCDxUgKWZ2cUg47Vw+ut8v3baZnKbH5lVaP\nD/1AIhtK1zGfpmImMo1YPkVW7W5HspV6eH1yLy1FqS6Z1ow7/+hywtlgLwpkXrcGU0Aq5J+iQivD\nsEdWOtGF7fj7DaQuqcDn6zr8zgxVjMR8Pjsotb/Q0yVkr262Vw1q/Pxm+R5W+XTSjhu/ZNe4ILnD\nt80t/l4WSe8DeIGmOOfG66pzqIOmxJpSbGwD5OGBE8mWBXVZYcs6B9fUlOWEKqvoAepSUuqMVhxP\nYP/g8Tg7OcF5OaU1+718T6VGj7RSgr8cvEoqFVnZLqwTZTTGFBRaoW1JGK5X8phgcF4RO/FmG2ve\nfsZTEktpiBBaiB0+NGx2EFtxRAB4HJvQ8GZ/z4vVis/v7tirxNLMeHosi+hHpxc8Xi45mc2ZLGq0\nMYTgaduOTbPm9k5CZ15ubvhsc8vddkfTOE7mJ3x0+YTHp+c8XkjJWuAjNUkFXOhwwbPdb3m1ueXT\nzS0vMhZz1ewJIfH4+IL3Tq94//SSpyennNYzJkMwdHKE5Nn5juvtmtv7O65Xa+53G7q+YZJJdkqX\ngslVCWMU0XWAtACCGzb4ij4ltG5IaQBOyUk4RM+B6yfkvMF+J5c6oh7oTEARcit1ZJ5/yfhaF+Ri\ntsUm2SX60DMy+EiQ5LQACmtFSm6MQQ9b1XHiUdmrKX2WsipQCVSGFfi4JbrDhvMHGf8yyV7/It/r\nh/HaLy7Kv6PFGPgff+l741LfjXfj/2/j5/+rQwDL3/mb3w1Z+t0e/9HP/mf0Lnvd/UPIY0InDQkK\nZM69uVvRb1boQo+rQWc896rjzX7Hm82OPmkWuubZ2SM+uBLx1OOjMy4WS5azOd54YpDW03q/5vVm\nxYuVnGqfr294tV7jHczsnCcnFzw9OuNqseR0nol+pZWFNHpccGx2W17e3fLJzWue36243YlGJITE\nUX3Eh6dPeHx2zuOjE06nU2ZlgdaHqlzrGlbNjtf7Da/Xa+7u1rT7HaqIqMzr1vs9u36D9p4Uhe8u\nQTEFamydCnQHLZZBOMyPWinUGABTjBzyAxxE5XXo4IXXpiBpgzH/H3tvHmvretf3fZ7hHde0h3P2\nPvOdfA32xWGwTbCxQ2gTCpQhSoWq1FEgpaIKahPShLahqCppaFNFaUkJiqqgtJSKVClqRCMqakgY\nDDZmMmaIbWxsX98z77P3WntN7/QM/eN53nftfX0Ndml0Hek+kuVz9tl3rXe963mf3/QdPvPZ/KoG\n5FH+5kHir3EV1sWWorE4eYbzLdZ1pAQpPqwjmv+ghQhiTx5QkCYSB3hVBY3bqA9rfYfO5ecUoIBX\nDFIX18VK+Y/SDn+lqvUzBcVX+t1XuoY/qPK9+PO+Bfi5VOivrdfWa+vze/2fP/Yj/JXv/f5hXDDg\nIrqOrjW0dUXXdZzXFdvNEqPAaIGNhiOd8tTKs2pbqqYjS0ru7F3hzpVjjmNr+Hhvj0leovBIB3Vn\n2Gy3nK7X3F+dc28VuhMnmzWtcxRJyc3DY565co3b+yGYF2UAtykhMF1HYxoW9ZrHZ2fcXZ/waL1g\nXW9xbXSOKve5deU6t64ccWN/n/2yZJTnSCwmotmrruJ8u+XR8pSH5wseL85o6gqBZ6JKUh8q5Ezm\nKOWR1tGZhlaIEF+U3jmOpQlFUZImQa40y3MkUQ9DKbIYjMpEkSR6KBq11git8FoOojQyusZ553fj\nuldYr2pAvvciJEkehBXUdGgp5nlKko1RUqATgU4DklVqj+5VgmywvbLOYE2DtQ1NFxxurGkHDVdr\nK4zrsK4FPvZZXddnCoT/Omhi/2HX+JmC8sW/v7ZeW6+tf82XHqGEIJF60CQXMrASXBuEMjbVBmO2\nNF3FebWhirOHLRajJNbDSOXcPDzi6dkxt/b2ubYXnJcKHdrmbdthbMO6WvF4teSlxSkvnZ/xYB0C\n8qqqmZQTbh1c5/XX7nAtn3A0mjDLc7pY1Trv6VzHolrzeLXg/uKUl6oF89UK2zoOx+E9nzl+mht7\nV7laFhyUI8o8QWJpbMM2upwtqi0PzufcW8x5tJwzX61IUBQqJSPn9rXbALz+9vMc713h6pUDJpMR\naSIDHqHXpIYIkhSkaRoBgf7C+Gd3q3vrxV7Pvh+3GLezWvRRX7wXPfpM6zVhkNfWa+u19dp6bb22\nPg/Wq1oh37hxMLjBXBaN93Sto7aerqtBWLw3gVrgIhjFSDoTeKreG0SkkVifo3U52CU735GmiqLU\nwLtfnQ/6OayXV6x/2O/C51fl/sa3vInER26vDz61wXFP4qwHETodQuxEC8K/OaztKIq+XR+y0B1I\nwkf8QPhfmuYBoCKivCGB/ymlQooAUEq0RKoUJVPSLDgoARR5STkakWYZrUgYFQVJlpIXBUXkiGbZ\njlOb6DRINiaaNM0RIhtAN1IKnAnahl56jAtsVO93+tMuArnw0NjoW2y6kEV3hjoKa6Dawf2sB741\nbYtq/JA5B9DPbo845wfa1kWHqWDx6Ki6TQR/BanAnp/fw4Z7MXx8eJ1Ea/7cu/481tpBO3qz3rLZ\nbFmcz/ntD/wm49GEqm1BCeq6hcgpT5MU0XXkWYZKAk0xSzPSJGE8Cq1OrTV5llGUReDAKoVI9QD2\n6mlIEBDNxrR0TRWERJxns9lQNwHNbL1lWy+p6wbTBfpaQMsbEPYSEM4YgxSRGtV1wUrC+8G8JJA5\ngsuTsxZvLcL1ph0XEPO0wW9ZgNKaJM4RPWr4HSU9dVMjJJ9Ga3rDO97BJqKxjTGY7gLXnt1e7zEy\n2gsKrZmOCqajGUU5oxjPyPOSpBgj0oAk10nJP/q73z28hhBpsIZU8gKVLA3ezkpgTEvVblhsl6T1\nClWvOWkCvaitO6wX5AhuHR7x7OE17syucjw7YBZR6YkQtM7SCcN8s+B0veSls1MeLBc8XpyziVKp\ns3zCUwfXub1/jeNyxvFkj0mRoLWiskGvoG0N5/WG082S+8s595YLzqqKtnYcjGY8fSVYOd6ZHnI0\n3efqtKRME7y3dLZj0645b8Kc+f75nPvnSx4t5pyuzpFeUaY5mZckQjF/Eir3j20/xoPkHtPZiLJM\nGUvBZDwmTRQytqJNvw+ExkaHOGywAXV4TJxHO3FxJLqz9dTdZWEQD3gb9uKXPP8NvNJ6VQPyv/NN\nfwHT9abvDLzHtjZs7COMbTCmCw+c6aIQf6SbWEPbBuP4ENRtULWOQvgiil5X1RrhPbY1n/E6Pp/W\nvyqQV78+0xz64t//KG3r6XFHV/WUDYlAY20g0VsjCPEgHd7DuEAFkEKSFSOUrLHORUrQjv/jvUeo\nQMoXHqpmBSJQiEQdvX6VuETuF8LjnB4CWP+xekSt9wLZ6173n324DwEpGcwQ0oD0FTpYIuqAKoV+\nzlSQ6IQkTdA6uEzlFzi1RVTRUlqj04wiy8nzDCkUxSgn3Q82pYag35zoZOBaSykJ/qrxlvbCBL43\nRfc4zxDILwprOOeoIs/bGDME7BAk4mESVbX67/3g4IA3PHebLNntFY+gbS2JVmR/7htZnMGHP/op\nTs83PHqyYLEKh2HTtDi33CGYraFqYFsbtsvAlw0BNyCYA+rZYZxFqAiIYUcF8yLodXtrQQQ+bvCc\njsmQVugUZuMJRVEgBGRZFCbJRyQxAUvTNNpyhu/Ydk305g4GJQDGdDRNRdO1mDZoL7vO4YynbSwm\n6inXtg730lh0omnbgN4Ougd9kgP5KLQ7nTdc5CGPyn2cCH/fNhuM64Kn9Oe4VKJIkowkD8AopfNL\n/35wcESWBebAjpam8Lio490gE4lPJEaD07CNRi5VK/BO8tTRMc/uX+Xp2QH7kymTskC4fs4MW1Oz\n3Cx5sD2PiOgnPFmu2VQ10yLs6aevHPPs4XWOpwccjWaMyhKfWFZmS23D+51Xa07OlzxYLnjx7DFP\n1ku6OmGvPOSZqze5fRisHK/P9tkfjRhlGhH54K1oOW/WPFwHVPf98wX35gsWmxXWesZJylRljFQw\nhiljAqaiAYQxHdva4cjYthuUFqRReS94KDiE8FFpUIYzYFA+iwh9eqc3f6kdbfEXetDBfMR7R+c+\ncyx6VQPy3/uB/4QszynyEaNyOhCny3JEVh6T5SOm0ynXDq4Gp500G+hLaZYEQQap0TIFIdBK09RV\nOKiiss52u4lKXQ0/809+8LO6rpeDqv5VVqCfC6r5s/ndlwO1/qDP8JkAbH+UoDy7LpC2ii8GSiZ4\nJxFC0zaObgtd3bGOdKamNVSris2mAp8g7RilFEWRk+XJIPCSpJoWGzAD1uGliF2THdVHuuDdE8we\nog2aciBUmPP0D5CI3rXeQTRdcM4jlBrs+Lz3qASqraMVAmtB6YREJ3hpBrXD0NnZOUwJKxD+MgUi\nAD6iQEmk/PRWfcETOf5ZTwZZzGBBGcQIfJYP3Mg0DeCSNM0p8pwky0jzkizPSLQmjQI4eZaik4w9\nkQfzA6UQSpLqdFAVih80/FmG22EdeNsEOk2s8PMkQyqLFB6BYXrY8QVJRjG6ikyKXj6AunZUa8/p\nSc3p/Iz79+/z5PQU03UkIvqYDww1EUwlnMN7S7g1O/5tG+1Ufaz8nbPBZrWuB4MAYxzbRcM5oKQI\nymwxEfMupxeE6fezcy4YUKSRgmJ20qxhdhj2bJJI8qwkTUN3ZDoaoaOimyp0CIRJsFx01kcVOD90\nH7wHKUNFVTcVn+Tnh+djNBrTuWhwYCuaVzpaPDs/HH/x/3fPZGsMmRQD1SpJLwfkw8MZeVoAF92e\nLNYKjBNUraHaNpxXa5abDefbFVWs3JUTHF055taV61zJR1zfO0RkwcjFxK5JVTWsmiVPlufcWzzm\n0fmCk+WS1nrKrOB6DKK3Do64NTvkcLLPdDxCamhtg1WO9SZ6K69XPF7OuT8/5cnynK117BcH3Ll6\njZuHVzmaBXvV/cmISR4od8YaNu2Gs2rJg/kTHkfxkMfnC06Wa3CWcVJyMJkxUzmFSsjSjL0ivNbh\n5JDJaMJkVKAzjdZ7JFpTjHKSGGOUDt9tnpaAHObLwRAHZNyrWvp6dOWMAAAgAElEQVThZ0IQ9zK0\nPiCtAZCglEBKhrnyK61XNSBfPTiMvpFgmhVtFdoJ53NHXX0oEqwtTdMMhvKDgo0Jjk5ChEPRRkNx\nlySkaTicINiSleWIoig/q2u62DJ+tehPf1gw/P/KW36l33slANsfJSjbLsdE5RqBoLOhKvIEJSqv\nNDLNGM/CQVLahOlUYo0EB3ZbYVyLUh6VdCgd0JNJIklHNVmeRT3n3db1UT1IRSlV4SXWxarGxjbR\nBTCFjUpLzoJvsyC5aIMMZa/m5bxDtOWAVFVKo+JrJj4hikChRJBQFEIhvMRhcAQlrv5QbbsYzKQF\n4Yb2bI9+HT4HTxDoWOGryMMXeLnd0faGmCVjpd9zdnf8XmAQtsDraIOog9axUiitSWNbTmuFSlKy\nrCTJJty6+Txv/rJrtN6j4mtVXRUs5fBYoxEiZ5QkpKRgOkTUEcsV7M/geJpguIHOb9MaqGrL6TxU\nmHdfOuHui/dYrraUxZjpeB/JkiyVVPV2qPC9c7Rdg7WBl+udBRekWLsYkK2xrKuGtmkwriNxlviF\noy8cbUKIQR88JCAOJyWJ1sOeGMQ4vMPWhnVdA55wObtzZxBzwceqWwx/V5F3nShFmmWkWTqgbPt1\nuLeHlP1ntJjG0rg27jEZRh3CDzx3pz0ySbDW0wvf6ESSpwlaWtKoUjdKR5feRyVZELTwQTksfNkG\n07ToRNLlCZXP8Z3GK40XmjKKflyfjLixd8jRaMT1w0OSIofM09iaJibby2bDo9WcB4sTXlo84dF8\ngTGSVObcunrMU8fBW/n27CqH5YzZqESpACZrbB0kOU9DZ+Xe+RkvLR/zYLGkNYpRMuO5wz1uH864\nOR1xJbovjTMNODrnWXUVZ+2SR6snPFg84XQRXuvJYosgZ6wTrk9mHJQleaZJpaJ0JQfRFer68VWu\nXzlkbzJjNpmgtKQoUxIlYywJYivOBrlSHw22fdSr94Dv4tio8YGy5W1sbVscRJ57f55YnO2iv3rF\njXd+Ba+0XtWAvF3WOwEALtN4ujoIFmiZoPJ8kDVre6k7YwaxgHHksyEMWVJd8r5dLpe0zYqmgXf8\n6T/1Rw6yf1Cl+f/X+lxf73OldH22/+1n87r/ATuO5bd8x38WIP9R3tS6YJvZC0QYY2jrE6rNI5o2\nmqZ7gW0cVju8g1Hm6WyHcw0Ig9L9QSiQ2oc2Yge2VfQtbSeCfrOXLrQzfXxgACWDRq1KBX3jVxhP\nEjWkhbtsj7frMUmkH9PLWvQoSucc1tS7maINfsHOiTh/THAmuYSm9D7q4DqQ/nKguLh0IociKE2D\nsUEqdcjKB29fTWj5qjh/j+YLL6O4NHUVZSJ3CebF+WyRxIpKSLzQQEKaTjl90PHvvusbENoNYh5K\nyNiqFFEgpLexdCBCS6+/cxJP78IhrSIToAvFKEp1PnXzNu6tt/Ee5mdbPvrR3+dTLy1ZLh2j0ZRR\nnFF6JzgajcmpSKiwUuBUUJMysd1u8WQkUcaz16wPJhh1W12Qzoy62F1L04R2dG/icXHmHmb3FnzQ\nQe+DtLUhyIc3DUHYC1CqQAqBkkG2V4pewEPh8FiZ0nTdJeTs/YcPOTsP+3VdLWib+pIP/DCHjhgL\n0dVYDCovMF2U9Kxb8qTDVRWNC7/XbS8nd/NHJ4xHE4osZ7sKQfT68QGNbdBaUxRjRtOMvb2Sk/NT\nzjfToX1f5gXTPOfq3ozJqCQRAms9TdtyHrWsnyzPeTA/4978jNNqS2MFpS65sX/Ms1eucXsWtKWv\njmeM0wIp/WAEtGoqFssFT6JG9YP1KY/WK2rj2E9mPHv0DLevlFzfv8JhOabId0pwIdGuWa/nPFnP\nebQ859Gq4sk6XFfbeWZlzrXZHkejMXtFQaolWgkKl7GNre0Pn9zlA5s1zXpLVVU4Y4LwCgJHb6Pp\ncNYghRqS60TFbpeSO29jx6Xn3Tobuk5CDp7i1plYQIZxzVd/PgbkzXYx+OGGNk9vAq2izN9OP3ho\n/0XVHCXBE8BebR00fI01WF8MhxOAtSX9gYpyvP2d3xzUvdwO4PKr7/vJ3UXFG//mL/vjeO8wzvBb\nH/zA8M9/7Eu+BJ0ksRqReJXhjB1mikHAP1TzTqQhG7+gMdwfjllaoqQizVISnZDlGaPRiDQfkZQx\nI5yMybOMvCgZlSVZljMp96KzT0Ke9aozwaDBxIzM2uDo0rbhAAKoG0HTWuq6wnaGtmup6prO1jhv\nBs9Ra6LUpfWxWnRR4s8Mm8sT4PyCBP7x+XBvTh6vsNbgo0i9E1AUeTT5jmpEViG4iZY9FqABaWKV\nZVFaIrBY22JsF2ZsgDEeu8iRMrZzsyzOdxPKmJB5gn50UFkKrTrnKpytcez2RNcGPIKzQZrRWbur\njGPQcs7i9OPw2a1FRGnKWPcPFbpM5U6sRshwcHs3SOiF14wtbUC6DpwLRgfx1XY0i15KMfq1uuBu\nRJ3hojlDkJYMCllBhEAFZyopovZ13GcyzKOd6AJvUgp6QwkhoIjXL6TACYWSOVplTCbFUEn2LX7r\nLH0/dygk+xm2N7uALEAIhxcKJSWutbioyOVlSHxCwqbwXnB0qLl+9EYMis0WPvGJx3zkwx8F4Hd/\n5/d49OiE6vwJqayRWgZPWi1I094PWaNkSp4XSKEpiglZmpNlBbLQpLGdO8oS9qczkiQlzzMSnYVK\nU+tLFa6Powvjdh0Va0ycx4d90db1MDfujKFr26ES2p05QaTIecf8bM7FZZzHuPBM9gd0kqioZy4G\nl6Y8fj/atmzOt9yfP0TrDO8eAAItBInOBn0k4yX897v3+Z/+wQ9xcOWIp+/c4ephaNP+w/e+h3d+\n5VtIU43BU1uHEYLWWkaTKZNpoBftZznTsmSUF6QiKHBV7YbVdsvjZQii987OuLs85fFiwdpZFCnX\n9o947vg2N0ZTjvKgLT3LC5QWeGeoTct5s+HkfM7D+Zy784cAPNismbcts3zKM/s3eXZ0yME450pR\nMi5yktiVaEzLpqpYb055vDzjwWrBi2dzTldb2litHpQTntq/yvXZHpMsZVbkaC3RSuKqChsBgabd\nUK3nLJfnbNZr2vV2wFj0Z7Vz3XAu9EsKGffJTusCQmvaexAudOqQEpTayWpGGU3nY9L3GdarGpDT\nsRxao8H4PT4cUtG2XZgBWUL7zsdZk+ldUCLww3kq5xBS4FHobIq3Dtv395Pg8qOVwrgmtOhUmPmo\nV2jJvukrvorf/uWf59d/4/2f9m9v/rKvCHrFbXBjcRgsF9qJ9KjhcKga2wyHl4OhygJwZoEQUNWS\nNMnRW8lqqRFopOtl4EJ7zRhDb8LhTDzAJbsKLH7pJg+BL00SyujGMypDq77I98nzEZNJAMDsT8Y8\ndfMQmZQolZP1aE0d3GG0DgAl50ScxbmhwmqamqapaP2a3/7H/8fw2b/ybW+n3lZso4C7dUGqsGtM\nQGJJ8E7hrAxE8nhPjGlju7hjMi6HpEIrNcw6g3PUKXVTxyqwobNBS7jqwkMWuppuyFbDA6BAhPvS\nJ1vWegQpQmQIE1qBQvj42WNSKAV5+sah21KWo4jolghvkLEN6LyjqkIbtapqmu4MYxd0th0QliAH\nXIOWLiDPnSVLs5jxR8vBtgttdxmSEikD8txisPSaxY5qUwUwnHd4K3GdoDUdzjum4wCmSdOMrMiR\n0qJEkEVsui60ob3noteKkIo0m2DtOa03WBeGmP2cu283WE90qArPolIqgif98GtWEPSX+88uFQiB\njJWctyIg7aVECYWwAq88o5HnhReOeNMbgzTj13/t27l77z4f+O2P8r5f+XU+8dF/Cc0K15yTxXvh\nupbWBvUpqRMQGkVAbFOoIVgpGdr2zltMZ6mrdpjJ735HorRE6QSZBHSykjJKY6bIiOTfP7rJaDSm\nyHPKsqAc5WT5hLLIyfIeoZ9jTIdzltVyzfv4yHCvZ5OSqgmJQlUHRHb4EoKdn7XBmWt/72r48bbm\n/LTGqwKSItxg60MLVCTDnvbmcuD4M9/8Z2hF0Ay/d+9FACbTnI///oeCboOQdFag0pKr126STxOm\nEa8xLYKLlxCe1jTUTc3SbHh0Puf+PCQYd5enPDo7o3GOTCbcunqDZw6vc3tyyPGoZD+eO1I5Wmfo\nTOAZP1qecf/8lEfnpzxYhmR+01kmesxTBze5PT3k1nRKMdKM8hwtxWC/uG1q5vWSs80p9xdn3F+t\nOFtXWCvZy0MRc3P/CtcmexyMx+zlBaM8IQRDS6s9qlf0sg2V2bLanlPV62B+0zRhr/adq96JzfsY\nfzxSqqjKFcxfABwuPC8WhJcEXw1Lixv4ys6ZOF/+g8eAr+4M+ebzoQUmJDpJht69j+294MHaYrsw\n2zPOoiIiUfZ+qxE1Go4Fg0+aS6AV5w04hyWAbTpraTo70KlevpxzfOnb/xQfeO/PDD9781u/KlwX\ngHQDaAdv0VJdAlGJCC5TStF0dvh50Hq1g2er1H4ArEgVDo+6NQjTIu0ua3fx84UDfdeK7RGj/VJK\nQasiIV2xtTX1SnI+HKrBCkyI6NsbK6AgmtK9bNYcX1v4UL1neURrRnvMLCfLcsbFLYoL9042hqPZ\nIeWtGNzTJFT9OhLuZUDJBrOHGGCsoWs6ttuKpqvxwlJVLW3T4Rx0F7xjp/ZGpMIESo/zvdB/c+Ha\n/aXPh7S4+O8vV1cTSIRXoTbywZFqZ1wQgBqhDeo4X1axheVRIu6rsGNomgYhRZh7iymCq2SJGnSx\n1TBC8ahUo2QANOVZHsFe4ZVylYY/C49zhp4s0bWnmOie07QNq9WG7bpiuVyyrWqa2uCaJqB/Zbj3\nPimQOqMcpzFwCiZSDWhs3XdscKAlCE2R5IiiHIBnQ5/uZWdIn1juTBDis4bAuB0iXAiGVrcgGX7o\nEQgVWoJCCIQPNo1K7zpho8zxwutv8gWvv8m/8affwUuf+CS/8LP/nA998Dd5dD8gtrUGVc+x1lJt\nVyitdqOFZnewJkqHLkKiQ+CVAh0BOy62+J0P9EuzXWGtvSBvGL63/jM9uPux3YF94V4Al7pgg9uP\nczzF8fD7j++9yLYOgEbfGgSgtEOgcQRJRqxAxKTcmgTUGKRF5WNc65BekqWSYlRy42YQujg6PuZH\nL5hYHB4dUu7tsVwvWK0fAfDkpGa1bWOHyJPkE472jplN9hkXU/YmIZmblCUqCc9FbVo2zYaH1RkP\nFqeDrvTJcsmmtWRZyZ3ZPnf2Drk93eN4OmGSp8io+mWFoTUty3bLk+2C+8tTXpw/4dH8jKoL961U\n0cZxdoUb0ymTacp4XKCUwtiObRMS/LN6zaPVGY9WS+6vlzxabalrx2G5z9MHAUR2+/CQg8mUWZoz\nSjXSB4T+pglnaG364F6xaYPF5KqpWNcV1baOTl69+IdCoJBqV/w57+KW31GaghVs+M4SnyGJnaq8\ngGEebSL1sPs07MjF9aoG5GefezOT8ZjxeEIWXZkA8B4ls3AwCEG13YZqUAq6rm/Bbmnahs50VJvt\nQI1StqWp28ESrutajIkVU+sJ/EQb5279jfmt4ZoOymAo8Ce++mvCpSiBilJqxGrAmVCxeTy+t6Gz\nvV9qrPitxUWD3tDKiNVsrMCcSRCIALjwCimiT3ni8HFGp6RECUHatzSFpybwMa01A8AEJEoqdGtC\nZd4ZnBc0XTPM0ZJUBhOP2D7vq2ulUjRJz4KJFXaQKvXCI2yC2zhqaiq6eF01Skoe5x/mBb5kuHf/\n4mf/EXiB9dFJqG9rRiBEMIhP0TonGVycMqaTKaNyTJJnTPYmjEdjJuWEohiT5yHTzrISkbtQrUgd\nDnsBQkh8t3MU987TNE0QcbcO66DrXPTIDt9RVVVYY0IC6PxACeqrawjz7sFUwAfrRu9dbNUzcHSd\nDWL2oQ0VMvGAZjW7dpUAZw3We6yIczBnWG2qSwe6Qg9UvzRVOGeQSmK6FCljxSHHIKaUExjvpUhp\nENKEzo/WjEfjeP3hIHK+Ah/t4LQKhg4IMnkQP5rBywCWahpDmo0iKr1vU19eFxOWnlt9Cf2LoodS\nC+FQkeI0eAUTTGGISZ9AhDGGk6F6jq81yhTeQeIb7kwtN774Gd70wn/Ix++e8/O/9JsA/Mz/81NU\nn/oNVvUS7wTShvtuTYeXYnCOAoFUYjAQydISZUB5H/WKw153zoYcRHjqSBtzMRHuZ8iTao4xNpp/\nhMvdhes+Kd/dH2cvB+TzxRN62HPqJVba0KwWvVmOwjvPuAzt472re7zhy97K7MpVknREkZXsT6dM\n9yaMRiNMPPCV1vwo3ze8z1d85dtopefJ/ITXve46AD/+v55SreYo3SESz5Wr17h+/RaHB8dcPTxm\nNIrazErSNA0dhk214Wy54MHyMXdPT3m0DNzhTWdIkoLjw6s8d3DEnatHHJZ7TEc5Wjva2JbfthXr\nesvp+YJ7izl35094sjpn2TWkWWhr3z68zTOzG9yaTDiYjSgnGcIH965tW3EanaNO1nPunz3mE08e\ncb6tqIxiNtnnzsExT++H/Xw4noZRn5JkSmHbJsAs25ZVtWVThW7apq5pjcFJidMKkee4ztJZ8BHz\ngpBIFJ03BKxKZGMIgXEWa/p5gcR1II2gw5GIBCEUVni0iroZJHgsQqck+hUerLheU+p6bb22Xluv\nrdfWa+vzYL2qFfKDj3yE+5HKFNqZscr0EivNYK+m+rawFLtk3AdhACllmJmmKWmZobK96A4Vco3J\neBw4zEmCikR5rZOg/BOz/V/gx4Zr+vJ/889yvphT1WvWmzVdYzDRiLyuKzyeJEvpOoP3BsoW6Sz4\nXpHHRfS3oKs2sV2pqOqKXrCiv/4wl3BEhlZoX1uP7iti2w1CEMbaQK3QEX2e6cF/VesEpcEnE4QQ\nZHH+V/odatOwiW3eYF8oCchJazpg11qH4IWKD6A1zK4l2btxGSwd4OvLmd78ZAnOY2NpGeZyeueA\n4gBvsBd9XMWGenU2zHjLdNf2ezkKuak72q4LrWUCbUTIWM3BQIdKkuBnm+c5OstJ8hFFWex47kXJ\neDKmTDPK6VXKPHRoiqIYeKlaKRqTxYqSqP4UbkWHo+1byE2Dac0A8mk7R9N01G0ztKacsQEJbDoU\ndhARMNYGPmzsYig/RerHeGpct4dgjMwbfN3QRWpRTYNIg7tMmY3IXEJTPcS4UxK5j6+jabo1WLWm\nTStkq1BGIIzFqg6DodpGrnicjwmpwUgwBuXCd9+3tcNz6UPLXjik1yijefDolNnRPrOItMy8Y+Oj\nxzke4XUcsQik6AGBxJb8znoyvI3DezHgSNo2sC+k1qA00gtG0vNFT894450/AcA3/skX+L/f/yF+\n9qffzZMHn4RujrMLbLtAJAe4vnNiE7quRSQGlVq6tqEznovI2IHtEZ+P3tPWWhPb6OEZ6ELDHUEY\nKygpwTnqpB8kgRZBlS7XKe5lxVBgDfRk7LC3hNR4rxHOkacazRF3rofO01/9T99FXhRY20VufQCD\nYfyl7orzl2fId47G5KOcF9Wco2uvB+Cf/u8zjq7fxvsG7wyz8ZjDacnVmWaStrS9wt7W4zBs2i2n\n1YJ7i4c8WC44WS9YRrxGojOu7Y15bv+Am9MjDrI9xkWBUI7WddQRjLluNszPzzlZbrh3tuHBvGFt\nJamc8PwoVO53JodcnRXsTUqm4wKFYCsbTNey3C6ZryMae/6Eh5slT4yhNZqDZMazk+vcHh1wOAnV\n9t64YJqneCxSQdMZqqqmquuAfek57NYjdYlKJFma0bYbyrRAY4aWtZQJUkh0kobRagTcxRYIKoZP\n4SKXnTCa3J136pK3ck/F/YOmyK9qQBaT44hIDoHiokF02vfpvUdFhK7DD5zHiPbCC2gtVGuLX29w\n4jy2IMPvpWkybFztXeSgmmF2C8C37q7p7kc/TtfUCAVFmjAbFWSzCLtPAthpMhqFwJ6kyPwAreXO\nb9k7nI/trsrRtC1t07DZbCPqOWyIupsH/9ymDq1S52KrOXhDA3hjhlZ3Gg8v4S3ehUNyMu5nlOGL\nd9kZzgbDetFTdNrwYDiT41HggkJVuJcgdBIO2r4VSZB/RHiEtyhlEV6E9nX8iCKidZN0cun7HM32\n8dYPrl3E1l1o/bkIVvII4TBmty0HIJa3rFXg7xkTAtygyqRC4E3SFK2CYIbQoLWgzAOKVEqFVIoi\nz7HO0RlDvVrSzc9C4OwDZP9+1uOUHnjJAaUc244ytFB7j+AkSQaAl85TdJynF0VOWZZkWQjoZVow\nzkfsjUZkk+gsk+UU+T5JmiHTcmidDgmH6BHXp5jmHTgzxogzWj9nu+kwacHv/u6D8F2fKR598pP4\n5vd4/RclfNWfvMWzrztmtP8GPn4X3vsrYcb30oOKdVMw5hrCOUTELjgpMN6R7vXv7aKQhgSvkaK4\nkAT3HsZhDhx+P8zK8rLg733f/8h3f+/fIO/bcp1BZju02EU5wUE/4BWodLvZ/s7svQ/WzgiUD7Qv\nqUIo60fbz9y6wrtuvpOve+eX8u6f+gXe8573cXY6Z+3PITnFxwTS48FphC+gFVi7BRzIhCQmEz0Q\n0+ERqUArFeg+5jLOJImCH95ZsB2m7WjqGltthv2VpznT0TgwRF6G0s10hnU9IDAqCkqJtwqhBF1n\nSBPJt33rXwRgWy2QSrHdriPX3GOcIRUJxphBhcu/LCBvNw1tZzi6cn03npIpx9dvYX2NM4ZRnpLl\nBQY4O19i8xAOrLM0tmPZVTxanfJg/piTbcWmNWQyANduH97gmcMbPH1wzEE5ZX82Qcdra0zLsglz\n8vn5grtnJ7x0vuLeesOyrpllGbeuXuPpg2DleGP/gOPZjGmeoYSgMy1eNKxWKx6fn/EgIrs/tTjj\n0XJOte3YL/a4dXjI7SsBUb03DtiJUa7RwuOcxLYG03RU6y2bVcV63VFXEVegxyRaMsrA7lkUfeAM\n5D0gzo8DEl8qtRPY6c/iofCI4C8h4nkl8HhSlVygProQrKW6MGr89PWqBuR/+5u+mc4YrAlSez2t\nxhiL71qcC7Oarg3zPufsUE0YawckoxQK6yIAzDaR8xluhNY6zJG7jjxRg06t836gnlxcWo1wSVBP\n8hY2Tcvah2pOqQAauW+fBKSd9FjtsF3UzwVGo3JA+Cof50JC0rYNSbJTUrJWkef7HByUFEVBmqYU\nRUE2ysiKcMClSRqoGVJgOoOxHauzOVVV0bYtVRWqnLquqeua+XqGzAIEX4vA07XxurIipTEtpq1D\nVdaGis0FPY4B3u99ANpY66ICVtcXhpdk4QC6l6X/jfUIv+OVQzhktVIDWMf7GOwvWJAFXp/AewVY\n0kSTCTnMdonv3RlL6zoMlsV8gdaaLMsGbeDwfoIkSYZEAOOQF5I9CPrCxgQSv0o0wnuU8IFPOswB\nA+9ZABiPlCmhWSGo6l0yV2vFggBWc9YGD1QnB+pL/1rOORwCKxJ8lKtUiSbTO2lD5RQqnaELj0hX\n6GSPWwfv5P7JhPuPfhuA6zdf4i/9xy/wwvO3GCcLMuHp2hH37y5IxG3+7Dd9HQB3H1lOzpaUKeA8\nDoe10BhBUU5p28cAVNst2+2aqmpxVmONxDsXKEZD8NwBz6RW0AUMw6re8D3f91/yfd/zvQDcmO4j\nh93CQIcCNyBSX0l05pX+PIAhhURajVAhWOpkpzxlXce+tBSHJe/6lq/nrW95J+/+5+/jve//ddrq\nIb4Iz4dWBtNWTLIJ0+KQYpZiXaAq9dQoay113bDartlsq0Fas/Z1ROvHqikVlEnQRnfGkGoVkrh2\nMwDvsB4lBW3bDAlxv1Kd0bV9p8kRRtgCJ+QAEkqkYnEWuMrX9jSbzYYk0bFaD8nttq1ix6enzF16\nm+DRaz111aDz8Bmn033SpGDTdmyaLU3bURvHYrtFSIeJCGAjLDUtp82aR+sl8/WaTWfJVMa1vYCC\nf+7wFjcnBxwXM2ajEYkQdLah7lqWzZqT88D3PVmd8dLZGZ/arjirLYVOuL1/lTuTA65PQ1V7WI6Z\nZjmKkEQ3bc35dsGj+Rn3lgs+eRa8lR+sz9k4w1E54+beVZ45uMKVUcHBKKeIMqkJLrj9OcdmvWGz\nqVgvN2w3LW0rSaJGQharWEUIplIlKCkjuFD3NxEtdezgxt+P9EIhetAP0Y4x7msX1fcUSOfQPX0t\nxo4/TNvhVQ3I7/6nP0Ke5+RZRpZnpDG7LvICXeaRwJ4xmyboJCNNExIdwC1K5gNEfVdpCJRLQqvp\nguJP15nQ6qSj7Xbavs5+ug3W8dOvo21DhW2NxdhmeK1BvD+Clby3ZMqEqHOBsykClAUpq5hIOHSm\ngR36W2hB62rq5RY7jwe3BOE8zuwe4pfroxphBsWlHgGQqICEvnb9ecosp0gLJrMJqVToeNjnY433\nHeCDNrAzWG9oO0PVdbRtCGpt27LZVENFuVlV1LHds/NVNXRdS5peTmiyYgpCoiKgI/Bf5QAqQkQ8\ni2cIyAIxbNSQ8LiYpfZUAzHcBx8To67rItAmwGmUjK2g+D6hLQpKC5TaSVn231GqUlIB4YjvBmpa\nX9FDCATChe9RqPCZfdxPQojhoG19O1wHgBWB7+u8GX7WA4fAoYUJut0JCGGQAtrIFZ8kN6jNp2i7\nFlFf4dbVp/jEh36Xp+885q//zS8G4MazG4rxx9iuZ5zOX8C617F39XmeesMe3nUYEzowozuGK6Mt\nf+WvfhdST0COMT7ju//G9/DWt9zEtDeHe2aM5fx8yXrZcHa2xhNa8X37OACiQkX/+OQJ+7MrOAWV\naakc/K2/83cB+G/+xn/FwTiA7tyA3HfD93xxT1+Ubd2NcV7hwHI91twjhMeaKKACgc1gIU0EbQpv\nfMOEZ17/Nbz5K57jF9/9+/zu7/16/E5WyHRJXW8Q1ZzzrY5GFOYSdbAH+eE9koRCZZTFJIDh+nvh\nO4RUKOGx0uJiYutlio9IcpUEIZtxPqLuRwNxCWGwsovvGRNinC0AACAASURBVHjOUqWRx2pIVQai\n5WweAtpBs0eW5TtpVhE6QR/92Me4ffs202noDu34rnEf2p6JYFHxGRqPJrzu+efZNudsqi1NW1NX\nW7bbLcvlmjaK5NTULOyWRVuzqFvaDnSScbx3leeuBqOH2+MDrk332SsKVAptt6UxHctqzWm14tEy\nJBQvnZ5wbz7nvLXkeclTs2Oe3j/m5mSfw0jRG2dp8DS2htbVnK3nnK4X3D0/5ZNnT3i4CqCu2jqy\nJOf27JinDq+xX+QcTQsKDUlkzLSmC3TItmVdVzRth5OCtCjJioyD/WBycvXwCnuTCXmWkqUpSkry\nLEdIOWhZ60RHaVsZNez7cz1UzDKJe0KGJDGMH6AnH+zS+/jzQFzBX/6qLq1XNSD//E/9b6GtIwPX\nVceqQ2mNz9IooJ6Ef9OB6K9ENLVWRZgRxp9nWYZWCXmxT55nQyXaV1FpmqJGJUmSkiUJe7NyyNov\nrrd++ZtQhPdEhgDRi2YY00URiUDd8dYj2yLMAm0vOuEGFGbdng2CAYFvawbUsxPLIH5xAeHrve/d\nFoBdcOhfw3mPEb14hB/8o51zNK1j095jsQy0LHk3OOf4+H5peRTex7RoKbC2oWvrMNtEDm2UHrmc\nplkQ5+80e6OrpPsJk0lA8KZp4GZOZxmf+BfvGe7dX/j3vi2KKUQh/rpms9mwWq1YrdbM53Oqah3k\nUGMC4L0jT3Oc87jYPt0dPnpAsHo8Q/dQJCgp6LWOizIqtbGr7vvP4q0DY7He7pIhF1WDXEjcBsoa\nu8rNIxByJwAjlQ+ynFG+0LP7jpTsec4+MgPinu559QPY16Nsd2k+fnEOiFuQCAkck6qrKPMp/qPv\nfB13btzl5vXfA+DFT6xoRs+jijdzcPyViBxkssb7Mx7d/y2aVRDWOJwV3DgY8ze/9xv57v/8hxDJ\nsyTlEa975jrV5pw88jaVlGTao2XB4f4eqVZU2w1pKgdGgJCCzWYbBTlCt0eXOWjPpusw5wF5+73f\n/7f4B//t9+8oP4TZM8KB75OST1/D5xcXw3a8Jf2eiPfXOYGLdBkhIdUJEoekJU88Ugne8ZaneNNT\nz/Ge998B4J/99E+js5ukymG3S7LYQQlveTlRkA644JplomLX7ln0mLbDmBa8RYugcdypaIJB4JoW\neYLtalS+vfR5VCHxPvL0fRcwGj50MLwIBYN0Yx4/DNzhp75wjDEbPv7xj3Hnzm1mswlFkfP008/w\n/vf/Cm9729sABnxEv4QMco9aC2Tk3nZdzcH+lH2b4hQ0bcVyveF8NadcZtyfnwCwPDvlrF5w3jY0\nnSAv9rk22eP1V2/wdFTguj6dMh5niCRUxo1pWW42PF4vuLc85V6sah8tz1lVLbNkzI3xEc/sHXN7\ndsiV8YQ8Vu5ZInHesmmXQThkfcbD8zkvnj3i4XLFJia/03TC7dk1bs6OOJ7tczjJmOSBMz6YqviO\nZb1mva5YrbaY1qJVyuxwn2k5pcxDhawwtM0SbyRtJRmXBefbOZ1phtdyztK2NZtNoEM1TTOou4X9\n4IY90jRNNEuxw57pmuqyWp+zEZdg+aG//z+84rPw6kpnbsO866I4f/i7I0lHw2Es6Ft+blAikkLG\nA5DhQBQCSJNdT58wQ5YyKBplOrjyaBUUnnrBeL5ld03/5If/DjrJ0Sol0SlS68E1piyDZV+WppRl\niU4T0umYNM/IootImuVolaNViZT7kWf66QAlQXJJyMLYAO6pO0fV7QQ4uq5lu60DjcZa7LYXIvED\nCK5tw0awIohROOvCLKZth+orzXM60+FUgxCWxEkSrcgdeC8vVTDW2vD3Drzd0lQ1bS1YLh71F49A\nYIy/QOiAn/rJd4e22zBPD2CTLIvKSEnB0XQvtOazXhRfB3tDIYJrzqgkTbNYkfmhFec9NFUbZRLD\nw9E2DVVd03ZhXtVLIwaaW69XbfCmQ/pdd8JbSxCg9KEVaj3B4EBcMD+QON0EepSxQYGNsOWkFcHJ\nJe5VaUXk14I2HmlCYnBx/4YlcUIPHYD+ZvbAD4RHqGsobXnbVyd86RsNTx1/iKa6z0ufCp/xeO/L\nOV2MaZoFJ6t/RpFYrk2vcOP4FpMrNdtpqKyEWoLLeMNzN/jSF+7wG78JSTEmVymZXpOILj5H4dpU\nEu7Flb2c6Ww0qIZB4FxPpzlVteHw8BAcqERz/fYtVg8fsjwNAWYsS8oiwRGrfhGkNf9gGMvllvWn\n6bQLQyfDPA8EDrkzR3ICZx1aeTSh/ZggcQ7K6y3/1td9IQC3Xv8MP/CDP86NZ5/hzW/6Qo4PuiEZ\n7kdN1gTJzaZrabqWrguJclVtaZp2l0g7Qds0tG2Nt13EaoSA2lsxJoki0Q7TVmyrLS2fGD5PMlYB\nBApgq7ChTOgkCNdiDHR+y4f+ZRhRvPEtT3F8fI3pdEpd18z2JnRdx/Vr15nPF5ychCB6586dyzdV\nBBCi8y0uYj50InjfL/4CRa5JcoVTYI2h6hq2TUUVAU8thto1GNOixZjre0c8d3SFm7MZV6Pgxywm\nZFtX4W3HulrzZDnn5HzBvcUTHsQKeVHXZCLj9vQKz8+ucWN8hSujGaNxitZ9UurY1BuW9YaHixMe\nnp1wfznnwfKMjZPkWQiiTx3e4JnZda7tX+VgPGVcCKS0ODq2EZuzbDacrRYszpes1w2mCfiIk8U5\nsyKlp5e7tkEFJRZMU9O1DcvlOdvtOu5Z4vljgrgQoXtjjR10C7ztE3zwIsavgMENlbBjwE7YHlBr\n3QXBoE9fr2pA/qIveAbnPIkugpD+gJ9Q2KTCuC62IcPh2nUdvu3J2EECseuCkL/pWjrrgiyk27UQ\nl9bsEJSuN06QgeD9Cq4bv/hzPxW5ur2Vn0LElqNKwkBey2Dpp5QC6UmicD8EOb+hIi8LtNKkiY5O\nVYpRNLnQ2YQsyyjLCUomJFozGo/JixE6zrUmWUYyTsiujcjSACTyNouAAj0cpt4HXrFPgrdwL3IR\nbNYiH9v43aHT1hFgVtO2NabrBuvLvlo3XRf8c7uwgTpjEGoY3oWK1TfAS8O9S6O6EDZWvzjSRJEq\nDdZG5a6G1bphfh6zUGuHLoGSAkyL8MHrNnCJ/fB7TokhO03TdBgfjIvA2dSJZL8oKKNwyWg0IitS\nVFqQZtkwz/EytKGVkLhWsqmbiJDuqAavXYettnTO4ESQwvNK4qUiAWy36wJst1uapqGua1y3hnaL\n6bqhA9ObQAR1KxfbsH2msZvNiyzBdQ8p0jPe8sduU0jJL//cCRuj2G5De0/pFc8+teKrv1ay0S25\nnbGnFqzNKbWHrn0jACf3Cn7vowt+Z/GQm1/yVXzkpV/kC567Rq5y8ALVV78uVPXGWrJEMJvm4A0K\njxjEMTS+M2Q6o3MGqQVt1/LXv/O7+K7/4nuQ+6Ha3rg197Z3McIyUmP2zRRpHUbXmDAjoG5atNAk\nWkaApsGh8S4kVCru6aIokF4iVIrHYawnyxK61uLiPmzrDpVpOhcSnCQJnQuVgPIpB/F0e8tTGT/4\nX7+Lv/8Pf4kf/fFfo95+inZ9F5pPcJjHhFV5snREXo4pJyUuz8nKEdM8YS9LKLOQlNvRAWW5R5pl\nKKlIVEjwJaMdOE94uq5huVqwWq744IWAjLfDgWwMOCtRtsH5bUjiZIoRNQ9Own8juoL98T7L2Sn3\n7t9lOjkgTXI2YsO3/vt/npc+dTc8H/JlIkdegWhxxpPF2bB3J/zqb/4cwsQiyHtUltJKhxOeVY/k\n9461tSR6xI39qzw7PuJ6OeWgGDOaRhML5cLoz3csth3z1YqHixNefPyAk7piWUdTGDKeObjJnSvH\nXDvc53BSsjdNQ9LWdwo6y7Zacnq+4PFqyd3lOZ9Yz2krx76e8cx+GK/cnh5yvJdzPCmYlAlSdFjC\ndVRN6EScL8853254sl7SdJ5E5+QyoUzLoK4Xg63xlm21oa23rM/n1OdzFosFTbPDo/TFkohJpRAC\nSQQb4oZOjUcMvgrQF5gehB76003bBMnlgbv/yutVDciPzp/gXED/SRkI+2FJEiRC5KGllidoFSDn\nQ0YvLpq096IbHhtndFLtQCS9yooxchDxkFIOgKH38N7hmp56+iZ1XQ/z4rpuqOuYOTahkg9gsgBE\nCypIcjcr6KthIZBaDV/iQFvqkeRJDlKiVYISYdYrpELJgOQGgoC5lKRJGv10FUKn5FkR2saxTZUl\naaB2Fdkg9ZfnBWlSUEQ5v3Q0jmjhlMk4QeuMJB2TJhlKpmi9g+fDjpLSdWHWZawdZunOBMWxbdvx\ngR/+X4Z795a3/PFY5YdN3RmD7dqQFdrQijfOhG5AX5lYSxspQtZ58kRATCqsdUMyBB5rg6CHM+F6\nhIzasvEg7IynW7dsNh1CrIDHeCGxRB3t+CUF1bdwWDmXcLC/R91U3Lp1k/EoZOPXbtzi1977Syit\nmc72QGqyvCAfTZiV6WD52OMY+iVVihRpqHbjj7vWhEq+bXFNE0F5DZvtlu1my2YTDhPvNEI+5u1v\n/2LmJ59CTzpeeGHKf/fDHY8W4Xu8dVvxwY+8m6/9+m9Abs/wep+5eYZPfOw5Pvjh6/zGh0P72OuM\nD3/4l+lOPszVo4Lx6AX2Dp4BCTJZgZ/Fyw9SgFoFG8nI/Imt9x3ADcI8LBU6COUbx6ws+IG//bf5\nS3/trwGwlCu+5xe+n3KvRJ1r/vLX/mVm1ZhpUeDjvOEnfuIn+dqv+Tr2Jr0hjORXf/13mIz3+LVf\n+zU26yCn+B3f/hchS/FG8MP/84/w7d/+bXzko5/kpRc/xeueex0Am+0S4QVf9EVfyMnJgoODmJhl\nCusczoSuwnRUoBLNd37HV/ITP/0iP/Z/LfFFyZWrt3jy4IMAmCcfJ7H3yYTB+JbOg9Mah0ILjYqd\nOSccSgWLzK4zJDpFK4XUGUmcbWdpQl4UCOmRCMoLcg9Nux5U3lS8t8Ms0oFMJNJaTJQHrdahytZK\nUdc1J48fc/Pm66i3DcIbjo7DTLeqL7fG424kTfNBECdLFev5E9qqCc+PVlgl8HkCaUoTE1aXJJTl\nhKP9azx15QZH+3vsjQqmoxIVo0ndNNS2ZlWvebKueTg/5f7iMYtmy2q9IYtCNjcOrnH7yjHXDg44\nmE0YFzlSCqxr6XrVrG3Fyeqch6s5L52e8nAxxxjPJJvw9P5Nbu6HPtz1/RkH05y9skQJ2HQ1rW3Z\nVFvO12HfP1wseLJasNk2gGKSpiRZQiI1Unp6LKnWCVbFRNA5qtZQt4am2Yk8uaD6gozw3wAgjkhs\nseum4UUcYTkGa1UR3AgHty+Z0VmQnzaU+X/Ze+9o25K7vvNTae990s3h3Zc7qLulllpSC2WQmkbR\nSAPDIEwQNgsBYwQeewQKDIM9tvB4ITwOC4QXIzBhbDADg2dEFAiFQUIJ1C21umm61fHl+2449564\nQ1XNH1V7n3Nfv5a91vzR/qNrrbfeDeees0+d2lW/8A3XfmLPjmfHs+PZ8ex4djw7nvHxjGbIaTsL\nTirWcezEMU4cD6WJhYVFKENpbZrnjEcj9vf3uXDhAoeDABaoqgkQysa2ChxSIR1ZW0RjhOhN2vjm\nCmwV+stSquB3e51gZWV1EWdrXmswQS+KGl1cRFSkihQZT6vdDojcGHolxkS95YqyKgIFq7IcHB5i\nvaWYRGK6DwjuYjKkdILKOpyP1KoYedmah10bDiiw1TSU34VEyYjqVKFP7lSJFAIpdRRZ0LMyigim\nGiYiB7UOXF6jWySmTRL75FortEkb3m1vaTm4KylNu9OOcxr/1qwcmbtjKx2MSXBRiF+nBqNNsCwT\nAlDgBS6CqoCmdVAUQe50Uk6oChts8kpHlddguZKqHAbgRBnE6oM0poOIDrXCR7vBGunosXi8CBQu\nP9dDpooYa6W45bbn8MM/+J0ICx//sz8D4F//q/+N0zfeiqgqrN2P2XTQWDdKNpSm0DcK/SHrPCiL\nSEJPvtFTdyFSFkLhStEIB2itSdsp7W6Y16Wki9arHN9sc+niOTpmlWKccP6hR5gmoUd4obfFUu+b\n+dPPPp+2KHnwXMa9D7fp70k21hN0LMGeOPMYf+uNPY4Vz+OBc45P/dUhK6dP4rQlk1WjlSxloJel\naZCuNcYccS6Co33dIIkhSIQMVpCJ4F/89D8F4D3//N2UvQPO2SdpZRn/7g/+De9987tRA4lthwzf\nlp5W1oslVcA7TLKCaS/jfMo7f+SdAHzq05/huc+9heX1NSZkXNkb89kv/BXv+Dvfzu5OQN2ePLlB\nVVY88MCD3HzzLdxzz1e4/Xm3gpd89fFHue2W0/G+HZIlGRsrGf/dm05jVrb433/1D7lULCNXwhrW\nrTNMdr7C1Z0HUT4nkROUK3EqYUBGGZHKmZ2E/mBN3fOBGlcxR2/yDm0U2gRH6Zdyd/Or6fQAW9Vl\nURkchXzAMEgfMiTtDY7g73twuEe/v8ixY1voRPPww49xcHiV1eXl0GYqaj7htb13j3AyyPXHqpwx\nbZIkQ5RBq6D0AXZXeUtZ5cisBsymrC2vcurYMY4vrbGxuMhSNyFRkioK+lhvGYwn7B322T7sc253\nm0v5gHFuSUSHG3qBY3x6ZYutpRVW2m16icFIQV5MKKqc4SS8x93BAef7u5zr73Gxv8/IOXou5ezq\ncU6vHeNEL1o5LvZY6GVIG+wxrfdBlnM0YDsaVWwfDjkYF/jC0U3atNMe3bRLZgzKVJhaeAZPqiRG\nSoTzaJEgTYcyLxsQr4wWi84GkKLSGgmzqmjjnhbsN0E0LRcvQOm0+V5p3fhjy+sdPHE8owdyG1Bp\nipSaor/LEwdhUrWWVD5w7GrEZlEUSMa0oq9qVSVBgKLmzUaecjGWlMIhRFw4kdusdChPiKZkEWA9\n145zjz3c6JWGx87QoTW1IAj+B6CHlDJYujVCES3a7TadTptWkgW3KSFZW1uiLEuKCD4obRGFQSqK\n6ZSytBRVUDmqRSfanRadbofUZCGwEIKqKrGlIy9yDgehJGfLICqSl9PAnSwrvA8llaY/qUwoQ8po\nHCBmYvgef0QQoxZk8YCNCPdAjI/KNCI8Lkk63MXLmvn5hZ/7xxitaUXghxeQZS06WTioW52gTZ2p\npHHGSeLhLlUwY+guL4SeT9ugjSEzNbI7xUZAntaaoixQETCnY2Bivcdaz3hagFLkeUGWJUxHAwbD\nA9LYm/cVGN1m/+CQQTnh5V/3Ivb3RkhXcO8XPwvAQlvRWdkAFLZytIzBlgHIIcTssPKeiPIMh1lR\nHiJkDsxoVs7VlDmLTCGIcTjKcoSNOs4A/fFXWVte5WOfGzEZG/7fz/WZHLTYWLuBfXcSgN3DF7B9\nkPLB/1iRym0Oxuc5fabDm795lZuPP8jZjQDqWkqv4Kttdqfn6Y5PcufXv5Kbbz5JmilE3kaa+f52\nKFUH9SwazMU8QrT+WaBzGLA50nvaWnJ2M3BTf+5n/jXv++TPYNa7DO2YyxT8/Mf+A29/4/ewPFLx\nvrX0eoJiUnPUDdNJzsZWipci1G2Bl7/y5Vw4/ziX9vaYjMeoNGXr+EkmBWQRh5EYTVlW3HbbbZSl\n4+tecgcf+9hnGAyGfNPr7kbWvsleYIRHCUuyqPjmVyQcW3g9P/3BP6bQoeRbdtepOEVn48X0r3yJ\n4uAJuuyi7Qjjxg21LpQnQ3AmPThsoCLNbaVCBMxD6WzDHKlHWUybsqSr+94+WlZaESiTrqB/EHji\nTzzxBDfdcgPDwZCNjQ2WV1cYj6Y4P2I6HbO8FMr0k9FRX29jNL5wKC8bYTCT9Dh2/DRuPGGaT5h4\ny0R6BpRIL1BJSEQ21tfZ2jjGybV1NnsLLPc6pMrifdlouA/yEduDPbb3d7l0uMPO6JB+PkXrNqfW\nj3PTYpjX40srLC30WO52MFJQuYLc5QymQ/Yi8OviQZ9zeztcGAwY5wUmyTizvMVNmyfY7C6yuRSu\nayFL8WXQTphMxxyWh1wdHnClf8DFyNs+HOdUJXRNxnJvieVWj17aJksMSpfNPqe9YOo8iWrRbXsq\nr0ClWOtpx+BRa0Wr1SGVBhkdw7yrefC+SZy0ThAitNlCsB3aPdUce0VK1fz+v1phkIQQnWBLqqoM\nYEPAeolNQm9QKUVVBTpDd1HQ9WGyvKt9hiKAyxORuiaQ5+Nm4tyMx6tNw5uhstV10W5CuqgmJVA6\nZMJ1H8ATjvAgr9hqmv5FUTCdhmhvODqIYLHwunWmWdMSGsEDwk0bwGrh8QKFF37Wsy5GDAf9yMdu\nobWm21mg1+uxnq03QgRCaASSzIigfOYFiUkbBxuAJE2imErVAInKKvBWyzJvwF9VZZlOAve4KEsm\nUY2oyKdUzQET3vdIOpg7kM89ei9SCLK4oIfDMe1OzIaURkUakUY0FYw6W6x7ykZBWRWR6mZm/E8p\nKXU32ArWNLfoh2x0N36+CSZr0e4u0eos8Le++a2ce/Qx+rtXSNoKW4zj49o4b3jJS18a+NlVgXMl\n3k3pR7qG8I63vvUtaJXEa9MNWMNGE4PwGQXUd+C0WoqxxZUyBGxxThveu6uwvmwkM8PanvXTdXYD\n+/tDHn9ySNpaZTgtkGaNy8UGe1WQGdxYO8n44n3kO1/i5a+2vPluw2LyBVotWFtNGlDaIIeq1Gxf\nuJ3Pfnyb7/l73wq6E1CgkTpY3x/hc2CuYhSi/Xkxlfpzr2FYSisUAlnDSYE1s8AP3/EjPFI+SL/a\nBiOgX/Hnf/lJvuW5QbBkeWWRe+99mGI6JUsFWcvQ7RlWVww3nDk2O8C85Zabb2S3P+C5t93C8fUM\n+dzncOHCJXr1+hrBxXPnkTL0kT/3+Xs4e/YkRVlx9co+7VZwAFLCMBlXaCNIFNywcMiJV/bodt7M\nv/zlgB+5dNCiu/kiDoaX0CeP0Tvepz2+QKu4grD7uCL0KCs81pYBaFbl0aDG4yvdmNV4F1D5UoK/\nRjzHlxI35zLnRQBIOu/BSbQM2fJoFJKTBx98kFd+/ctZXukyHA5BOqwreOCh+/l/fvd3EDUStlLw\nm7PXKaqcTLb5hX/zC/zQj4aqQ6u1wPLqMeRCRZ5PGVYFo6pAuwIrBAsLYb6OrW+ysbrKcrvDQruN\npKSwwSXqYBwVuMYHnN/f4XJ/l6sHfQ7yCZnpcmJpi7Mrx9jqhUBhY7FH1k5JjcL5krIsGJZj+tND\nLg8Dy+bJg10uDvYZjnO6ustWb4Ozq8fY6HZZX+jSqb19dIW1JeOy4iA/ZHfUZ7u/y+X+AVf7h3FC\nNQutHuvdJVbaPZbaHdpZRmYM0gR2DEA7yZB4UhkAuVbUh6Y7gp0wxqCQDRNIRu/5GpfUfI4E96cQ\nyMY+exnWBkRKXQxoZ5Djp45n9EC2srbJc+hszvvWOZQK2bGNlni2FsioJ8HRoBqp+aMevEyixGGM\nhJRE6HiIzMngiZq9fc1QwiFlPJCFQFJC1HitM7LgPy3xPrh7yERj1IxXWnOGS2cbqTWHba4RQFKX\nLQ1JS0cYPVgcLupiB7mRYJF4OCzw3tM/uILWgRaUmBCUZFmLLEuZeBqbRGenWDvLkPsH+Uz6UemG\nuqQlqCSllYZNTgiBWl1rLPuiXgZeBEEOCNxeay0y0fB7s7l7wa3PxzlLfxo28vaSQCcBzNBq9egs\nLNHSGl+WDEYhgBkMBxwcDDCmTa/bZtLfw3lBWU2DiEIUGXGuRIs9vJtxsGuqXO1ViwwHf+kUOkn5\nn/+nd/JNLz/DC5/7CnRbhNMeqLxhffNGvvfv/iCtlmB5sUfHrHD50jk63XC4L6+u8Fv/9l8wyUu6\nCwu0ul2SdptOp0M76zYKT0E2M6PVaqGUZHm1hzIdjNHNgWajbKiSKpx8YRabG7Rehwma3/jN32Ux\nW2VSjhmNK4TzZIlhvXMhPNf4E7zptSlvf+tzWFo65PL5J3nyqxUnttZw4hSH47AOdweeyztjth9T\nZOlt7F6YctNtPWw5IVGtOd34sNYHg1mG1XzOfraZ1F9bCZOyoJMkaGdBQo27kyhelR7jjoUuwhgy\nm9IyCRMxbNSPvu3b/huK3NPOFN5WOJ/jZZvKVbzh9a+mjFZ7iTFgSxbbCd/25ruoJpbF3gKpMSEI\nAEaTMbfd9jzAsrd/yMteficyIop3ti9z9WqwaVxcWsWYlNIFhb8kbaO85zV3tNl412sB+O0/+ms+\nf89jWGGYqmPkcpNpukU3PWAl2WfBhPlR7koUrKmo3JSynOKsRZUFrva1tuFeFTLQsrh/do+srx+n\ndr4K3PhgdO8sSC/RMkE4kFFRajg8jEF7FdohwqK05/yTu3z6z+9Bx7nwxdHgqapy3vCmb+HE5mn+\nwY+HNS1EStZbwlhJZivSqqBVFnS9RaaGreVQZl5ZWKDbSVno9UhSRVkVVFhG1YT+KBx8F3a3udDf\n4epoQG4tRmZsdje4cWmLEwvL9Hrh+tvdFC0VRTFh6qYMJkOuDHfZ3t/lQj9ktVcOB4zKkkQYTi1t\ncsPyFlury6wudmm3FDXF2rkppc3pT8bsxMP4an+fvf4QG9kY3VbGaqvLRm+R1XaL1V4nAl0VTtpG\n0Kc/mnDYP8CVFl9aKjHbq+t7uxZHmo7HQdLYWaqyrjjStAnCreGa/bze4ytnmyPGOUeaJtQ69m95\n/SyRmR/P6IG8cmYl9vt8zIoarDJY3/A16yzX2hn5PmhgzxP2w0QVtsJVVSOLWUU9ZC89TgYJR+ED\n1cNd50AmNU3JtvQeBZi5iElKUWN2EQKyVOOsxMdDu6xKbBWuJ8lajQWhECErbAQhiqi9LCWiNs8w\nIvQp4kZeWRvt3+oCqMUzocwt+bTAxpLCcBj0sOu5VEJhklAW7rYDTSERIHXo41a2iIeBg+gJXMMP\nnXOBYys8UkiqikgBCz1ziEGHdQ3ytPk8N84wGk5563cHYvfLX/EqTp46TdoOQYwQoXxrQgs8vJ4P\ntOU8h729IU88+iif/tRnuOeev+TipYvs7ASepfRB5scwGwAAIABJREFUljIKeTMpY/lJzETbnXNU\nFlrtFq/9xm9CSejvF/yz9/80P/be9+JiK8AbyXNWl1GmwpWws73Ncq/Dl7/0Jf79r/86AKu9Lref\nOsZpkXL5q/fzwLhP3yha6SJ5Pm0OmJpnniTBdzhNFUIHZSsZH5NGHmW3t4jyrWhwomm32yilyGL5\nXlIyHOU89PA++wcVrVZKYiacWH+M19wVzAae9/zbuPXGNVaWe5hskfWzHZK0haiG5MMhyypkAGfc\nImAo2cOWKQ6L1CXDw0MGtmQyrTMKyPOykf6DoCxW2AIdAxgnPErVtnwFWdLGKUPhPTJyLwFQnsoA\nGGRh8F4z9WCFxkb/a2FzsiyYJUAIpqzLQ1/e5qQRyxD07V3o0VqLwqOTgBMpY2Dd7bYwUlKWsLS4\nGG0rgzb71uY6vqlGaaZl8LKWQjCYSIwKFbObN8NC/O433czb3nwHn/7iPn/yiYe4vGuhd5Le4ll2\nBg9xZXoZgFsXbuPYRhdb7lAMdilHY6q8YkyBj+9RiQppC7QscLHn2gxpmqqVdY6KoH0vvMA5GVpw\nCBIR5mFUjKisRWqDowrcVm944XNfzM998JcpYz9aIvguvrF5mR979/+I84LxOMEWYR7SxLC+vIUS\noqFEFlWJTAytTptuvU7bLbKWRihHZQNnezDssz8YcnE3VJC2BwOuHgyZOAel5tjSGjetb3FycYn1\nxS6LC1Gi0qjgAVzmTPMJ/eGA3f4hF/f3OH8YgvLRcEKm25xc3uDm1U2OdRdYbHVYbHVRwjae6HlZ\n0R9N2BkfcPVwn4v9PfYHwVLR6HAPraZdVlsdFrKUTqeFyRQmkWHvqzw+bjxl5ZEkWFuQlxUVlqrM\nEV6gTfjMvAAtFSbtNC0bWe/VUT0OYvYrasGgWlMDnFVzSZ8IZklzAfj1xjNbsu7aUHt3kMqkKZkJ\nIQlQCRczTiBKKeJr0QkZS9OziRGA97FHWocpdXpHzQUNzxX0hcNj7uNyc00nb9nAllVUc4o9NDvr\nA9XOVPjYf6vmXoLoQxuvq7AltUC/czaUROJ1GZk21zijVIC3HhfLodIFrSQvBFJpjGphxDJKhYUx\nU5SJpXM7DaAmT8wyc/YGMeOI8m8Bli8xJkUrhZAp3ptGjtC5wKMUMpT6SgRZkqLTtNmwrbWIOkOe\nGxs3vYCf+l9+gnIYXaikxPsc7UwQ1IiVhbp6UX8+tgqH9Ppal43NO/i6l9+Bc/89SszW7sWLA544\nfy5WAMKBpiIVrhWDDhWNIBCOleWU4dix0kl4xatfy59+9LMQ6WQkUCnH448+wpMPXWBaTjkcFiz2\n1vnhH/hRAD738U/wxKOPoZ3mFbffzgtPL/Ok0lRTxcGgTx57aaPREFvFnv7BAcODMQJHnufk8eBo\ntbukWUZpL6ATFTbfCPxRUjf9yTI3+OQmVo+9EJGOcfYC3/vd38DLXnKam24+CwRFpslkwqR0TEqA\nkpGvg9n2XJl5ENcQQPRuLmt3kILhOKztrz78VdrtNtZazp07R68XHMN293ZY3wxgGkvF+rEVPvrx\nP+Mtb3gzW6sG0ekhtY5rsD5hwr1rovuNlzaI+XiNrHW9IzCo7kvPXJaiGIubbWBSaqQM2AAglBNd\nMJoAAk/ezbWBhKYqQ7BcCNVQH70IalBeB2/ssNRDtmNideWGE21+5T/8Cbe88A7e9DMv48tfGfNH\nf/hFLl7xnLnxNUynIbMaHTzGY9sTBjuHrCys0ko0MhlwGkkZ3zdKkrZaCCOo8Ey42iz35d4qkzx8\nNuOxRGAJahUS5cK+J71A1LxiXTCeTshzi04J+5/XHN9YI8fjstr3/SjtyauS4XREJ0lxESvTMopk\nZTW22KY47+mY0P5Jk5ROrA2nLQPCUbqCopoyHB+yPzrkykGfSwchqz3f7zO1DuclN66f5PjKOseX\nV1ntdei1NEmsVOTlhHExZZJP2R0eculgl3P7O1zc3+cg4uCMTNlcWOPM+hbrS4usLC7QaackRuKs\noIjOUYdVyd50ysX9bS4f7HFhf5fBqKCXtFmLutjrvQXWel0Wex067QylZRBHKTy+9I1HtrUeT+B9\nt7ptBAUmAl3reyhrZWRphskyhJANKFbEw7e+1UJiQJN0hUxbosUsAZNSxEqZ+Frn8TOMsk5NzOyu\n4TsKgW00jY+OGtnmbDiw40+b/3zcnGr+6jygTakk9liD5vS1PTKApVWBJEX6cNCXCOxc9lg3kmU0\njfBV8hQbtxAhBeH48B7ETEowntwB5Rq+rqXW6gv2ftZHg5lzk5YKP9F4b/FiCrH0RQSW4AiWd17i\nrMS5IPUI4EwX6wXeC6oiZBmHkwJfGqxvETFwLCwGfdfeQlAlW+gtsbi4xMJCj5XVsEH3uj263S46\n03z4d3+hmbsf+Yc/xrR0jXNKzW0mWuuF3qtnnm3nfa0LHvvuVDgHZWFJkqwpRS0vtVlcvLH+Iypr\n0Tpoetu42Ze2wruKdqvDpXNXue+BB9g5f45UJAxGFVsnbwDgjpfcQdIxnDxxCm2XuHT5Ce774lc4\n3N9lPApzOi4ki6duo39hm/uvDFg7fpwH/voRqomg1etQ1GBB1cF6S5J22Ty2TpomdFptslaGyUK2\naivHd7/9e1hZ32DidNDito7JdEKn3aYbNX3/4y//Hr/9f/8xuXuEv/8//CB3vfom1pcFo3G/WR/T\n6bRpPdTzd602dDPmgXt+ZrDhIYidAOtbWyihGE/GvPAlX0dVFmihePGLX8z2TlBm05kmLybc/Zpv\nYrG3NBeY1QYacyA3OddX89f/Go4it+sK0vzPa6GF+UO7vreOjKfZ3ApnkXWgLgTYABZVicSWBJUv\n7xFx7eS54E1veh2//Ou/x198epmbb9nix370lSRCc+/n93jokQhAWm6jOcPB5i1c3X2CSwf3U1ZT\nnsPcfiIh8xU6CZKr82MtXaBuDoxsWMeFl5S2onAOZ6EUEhmTgSrf58qVK5w+cwZqjXsPsqqw5Mi4\nDnuto+Cx977nPfz9H/gpytGUIrYBtJF0WwtY54MoUFkGcRNj0EYgZcQyGMirgrLKGY2H7O/3OT/a\n5/zeNjvDkNVOqgnWSzbXNjm1scqxxQXWlzqhRGxUk1QUrmRUTdgfHnD5cI/z+9tcGvYZFCMyEbLo\njaUVTh/bZLXXpbfQIm1rjPEU1ZC8LBlHXMTlYZ/L+zts93fY2d+lnOS0jGatt8DGStCoXum0WO60\naSca7StsPg2cdOsoK2i1QvB+9uxpVlfXWV/bJElSMhMAdloFwZb6c6y1p72g2WN9nR3XAHfnsT5g\nAbQMBkYhAasa3nOzauequtcbz+iBXBTT2CiXzQ0HUcxD0ugGw2zjkbFXq7XEuWs3ITezQZwDIDVD\n5CAjCMW7xj7r6IiHafQqVQEFA4CJmtvg0SaIAygkUpgmWhIiZKfOOgo/DWV1FyTswDeKXnXRQ0DQ\nTI6vbp2nqmaBhvSSYFoY3KzkYtBW9q4Dri6bBunJ0iUIocFrvFMY0yFLw6HQW9kkbXdZX99A6wwl\nDe1Wl9XlTW468xxardlSCK3uKgpz1KAf1cyzjDaFpbN8eG7meqlEVr5ByhZF3KQa/W1LcA46Ou/1\nxut9wA5QedrtFCEkaVbXtgXYOniLvVcRAiRd+zTnE1qtDtV0zNbmKjff8FpG/QojNQ7Y7YdMcX9w\nwM7uASrRmCzjhXfcxGu+/gUsLqS42COS3lMKxUIJCXBgC/amOaNBzs7BHpcvharKxYsXefTRx9jb\n3+Pw8BAQ5LZi1O9jXbCN6ywu4VWgwSyYSXOw5AXk0z2ubj8JwJlb1/nDP/klVjcU+dTiigOmwwKp\nEooa9NbY7c0+l/nvrx01IPwodQnSdijf33vfo5w4cZLxdMTh+ICiqCjHE06e2GpkJZPEIJ1gOpww\nSkesn1lFCBmUtZQ48nrzGtHztorXymPOB+L134U2SuxVX8er+HoytE/nniPVDJPiIpZjWlYxi1EI\np5BOUEZKo5KaU5uSu77hjfzOf7qHfDrm4b/+HIvdKS+54zQ/9sYgwzkGPvuF83z+CxdY6XU5XdzF\n9s4Bj/evMj4Ia6LcewydX0KMLmHcgDt5XnNd1dSTiIhJSRNcBXmRMPQjXJVHIKacWbAyYnfvKqPh\niFZHhHvJWbRz7O9t81f3fRqA3/4/fws+N3v/CwsL5MUE7QqKKgrPUOB8jhES0zZ4DIkOWtBegvfh\ncaPxmCIfMcrHHPb32d/Z4XC4z3RvFxed1TpIFntLnOx0WU0Ey0awaBTGVnibNxasxXTC+LDP/u42\nuwe77O3tMBkPkdaymYZ52OzCwmRCO8lIpgYtwY6nlEWQxLyyH8rkl/s7XNi9yv5gyOBwAEhaSRs7\ndAz2Q9VhZ2ERv7hMmgbBFiMFk/GIyXTKcFo2yn/eS1rtFlnSZmlpiU4W8SDGUNXBkK2oypLpZETl\ngue5EjJ4CjhLFfvRVVlSRkvf2mnQe4/Cz5kchTapjXKt//ZDv3TddfuMHsij0cwJpaZb1MOLWi2K\nIzfiTMtaxDKCbDin4COydcYTDRDzOuqemXpfy7WsR1XK4P9LoPYoJFrMVL+af0UoiwsRNI5ruJiP\nvEQPCF+GHpgPvSvvHC6ClJROwjXGcp2Pk6AEyLr+LQRKmmgNljBmgugIyglsbN3E9uVwA2XJMhvr\np1g9fRNaG5YW1zG6QyvtIqNX7fJSF600aZaipUZKhTISbS3GVYiy9o4NcpdC+GiJSKBKxZ5yeEwI\najr66GbYFnlwVoqOXLZGhFEj4ENLQWvT+EK3Wq0GRAQCV4qYAZco5RurOuc8wpU10i2CAQMKf38/\noDUXFhc5PDjAmJRiWHA48AgbyvSFtYzz8Dp5MQogu8oh1BRpFsirCcORoRfRoZNJRWkLVJZQSkU+\ndqRtzcp6j04/5cabA2c+SV6JkuG9KS0pioI0yUCUjCbxcNeGaVFhFGQiIc+LeDAshIMmllbveOGU\nstxhPCqRBE9ij8F7GlT6fKn3et/X4wMn38N7zn+ApzmvaGXhYH/TG+7G2dgaikpDQffbkrVCX66Y\nTCOgUVLZEq0keT7FJCqYfMwBV64XGIRlMDtc6/+vvfbr3Y/14+rnns/0A/d/bm+ozTw8mLm16VzQ\nKpA1u8CDipzRumdthCDPHS98ccZfP+m4595LZPoM+4eK/uGIv/iLTwJw+8kVbn/xcV7/zldxdVTy\nkT99hC98bp+l42vkG4GeMxodZ2/vCvsHO/QHQ+BScy33PHiF5ShStr6R0tYtMixOKoQeo1yFtAIf\nD22JZL9/lcFggFAZiAJbSR555Cu8770/gY8HXw3Sq8dkOgoHCBXTWCIvyhGf/PjHUS7SQGN1qjaE\nycuAKxgOhwwno6DqJhVVLV1sqwbDI4VicO4yD375Af5GJ1hbIasgdSslDWNGu/g9nlzCpMgpvUUi\nuCpCifxKdT/3OYmRCqM8WgWcT5okeDzTIlJYFZRKkBclKn7uIws5goO4Nz0mJBUghUcKTyvLSGMZ\nWaXtBvthdKDMhXZfRRo9sIX3qBj0CilxVQUcVWese8Yz1mzo/QfHuMBb9i60auqAOcjo+phkPs1N\nybNKXc+OZ8ez49nx7Hh2/FcxnllhkNo2zweuZhNxxJo9sX7vfNQLloLqSBkrNOcDejk6muggKFJn\nyPPDOxtKxCL2r68TzSdJCt6jQ5c+KjzVoJGYycs6U5ZcU7FruM0eMERN3abP56mr5M4JkOZIk9uL\nwE2uf+TJmFYpSnUpCkledlmwG9x00w3cffcbWVkJNIVEtQFJ5STapAFV7qEsggkHQCo8Dgu+wrkc\nXNCXLq2jsA5Ve3IrhfUhIxYIkiRDEqwxawMKGe0FbXSHqcekCG5cRV5rM7uYxQYqjRCeNG0xHPab\njO/w8LD5GgjGFRGJWFXVHDxAzFykotZ2VZVMpzmdTugLDcZBW3xS5HhfxbJ2KF3mRck0Ol+VKrYh\ntA6GBaUllVDksO9Cj+wP/uhjLHV6bJ09w6c/+1m+6Ru+ns9+6jPoRHHi1OlGaeyBBx7gW77lW7nv\ny19meWWJwX6fUydPMBgN6UWgyTQvQ+acZvRabU5sHcfhKKuAVrdVnQEMMSpB0MI58CKwAwQ1UPGp\nYz4z/pkT7z7yuw+cfA8AP3HuZ5/yd73YorC2iFUm33zmTji0NhRFqGC1WilYi3COJE2Y5DmPPvoo\nN9x4FhOrKWHtyFmpiPny9VNL1nV2/LX6adeO+epW87PIVKhpiPXjnKPhNKcaSBQ2dqumEQvirG+U\nlIS0JNpD5fjet7yalnyIT3zuMVK9yuHelHZ0Jxs+BF++8BhV9Tk2NntsbW7xQz9wF1mhubwXMsxH\ntw+50H8Oj++MePTiLnzyt5rrtd2XcGEYWhTb4wHrHcvZYwt0WynCXkWUU6QD72oMjOLg4IDBYIBO\nHIgCZz1f+ZsvMxxXaKLZg5tTCgMmk6BkWJZTdiIWwPmcrz54H6oqI+0xZG7ex9Kqrx2tPJV3lNZS\nlcGmVNvQJ60aDrVAeR8OEK3D/eA8wksm3lLGioi0oe3nYllcVJbUBcGmiYpANK+QUjOqygj89Dgk\nvrSoOdCeFZ4KTyI1hQ+830SpoDkeFRkTk9DNMlpZC2MUSkpUBJMKVaLi9UsqjK5QUgVgsLANE6CK\nPffgOpiCdaRKIbTGadlUV+szp6nYiCC8pFXQrrCFnd/ekSoot30NGvIzeyB32r0GcZZl8/aLHqlV\n5PsF+lNduq7J2HKulFVLV85KyvLITVuXkD1FRMipKGf41Jkpy2C5F6xtBV66ORS3BEL/VESucq2y\nNKsyzz6BimgD6RVJdPqoP8Q8L1BCB5CanAHBIJSnAZzMmOSaU5u38tJXvoZjm6c4tfQCnK9IjKRy\nefOaHs+iMqSpZDAYs7OzFxDADU1MBB6dLRCiCiR9V+Ccxouk4d4lqaZBrnkfkII+UF/qechMQmIM\nne5MYAKgPyqYFBUiUj1EFHOvXAXeMxoPuXL5CrfcfFtDqVFaURRlQNmqIPUnUFhRIayjjKU4WzpI\nFMKHYENqQavdIkk6TX9Vm4B8dN4jVQC/VS4IzVTMIHACFUT8pUarNomWJImiKAuSKDKysLSJHedY\nZ3jFq+7iC5+/h9XVLQbTAduX98hjL21jbYtuawElU0aDgufc8ly67YzHn3iSPA/XfvbMjVy9epXX\nvep2Hr5ywN5kn06nG4RdFI0YXOqXcVYghUbgcbLEygo5BwAMC8M3a67hFDx9FWy2JoVvHijqk9NX\nKGUaxD7eIZWnLCdkkQBa5FMSoQN1JEa048mYy5cvcuNNN1HFwOzaoGF22NZ34PWv7XqgtKf7/tph\nbQ0KLBogmFIKoyQ2rovKKYyJBo4SKhWYHaJ0MxtN63HK0ZKCNvBd33YLN96+yu995B76+20G01DG\n3BbbLItj2PEpdoaShx7f5VOfeZzl3ogsov2XV9Y4tdZhfbHF809uzNOQ2Tz1UqajoLp22H+C/dEV\nhg9fYW1L0V1pgS9RecE0zqlJuozGh+R5znikQAYxksFwhDZdbjp5BwA//u5/wDvmaE/T8Ri8ZTQe\nNHtAUVZc3bmMGE+xPuA7RFwPztuGw+28jMDaQL2UUmBcsBytmj3TIYXASAllHl6jCmYwha2wESci\ndR10SXACX1q08xglmMb9y3uFEo7KV0HS2Dmsl6FNUrlArSO006wQWGFJtSJpJbSyFjpR6NpbWUpa\nOqGdZSSJxlkX5DG1AGxTEvaUKBOkMI1OsTL43SNEE8gFdUcHTqCUxnk381qnPg/CbRWAY7ZxeAOH\nSqvGTEiL2AYUM5/0641nuId8GMQSxAzoAVGVaVLOHbDR8koIatN0hAqbkRBYHL4KRy7RpL7JfiPn\nONTvA2q5/tX1elZKt9Bm7s9E3mQA+CwAprzA+wq8RYhgD2fjdl/ZEmTw/phai5YSI6CcjrBFTiuJ\nAhxGUNiSslSkrS7j3CKNxrked97xBgBe/Q1v4IYztyA9XDp/mVQrMhP8fn3ucBF1aauwKV3Y2WN3\nd4e9vT3yvGAwOGQyCQdHVUrysgi0krmevNIOKR3TeMC0Wq05QI3AJG2yLCVradbWgu7vwmKPxcUF\n9vP6iAtj98IFkla3WfYq1aGHLxVKCRazNitrmyF6jJvEdJI3SledhR5iMmGtvYKTFcdUzrAbez7j\nFnnXU9iMqXeUMkcZi/JtZAPUCDZpWiu0ThlPRhgVbirpDDqi1/O8IEkNrSyhtCMeO7/LsY1jYUNS\nIUN+5UufB1JT5MGm8o1veg3OuxgI2kBvi2t1Wh5wx4tuCZkmYXO782Uvbubl9rPHmq+fsxmy5ocv\n7jXZWT2cDEIbjloYRqC9fEpfaf7wrZ/inx3/cZ5u/K8nf5yfvPiBI6yCeVDYvDoRArSTaKEg2ssl\nkZblfEgxU6148R23o5RGuGpmf+0tQpqnoKu9d01laB4VPt9Dnj+U68dd219+akYtEV4TpEgJLlRU\nWCcoKzWrvMgQiNRoW1SsfiWy2Qu8kwHhbAEcqsx52c1dXnrr67iy4/mzjz8EwD0Pjxj1t5G0yEsJ\nsqLV7nJwYBBBXAuzfYAX+wFgek2l7qWveC7jcXCr6u+9iP7BkHPnHuCRwVdYcmM6SQfDHtYFVPd4\ndECS7DGaThBagxxh/YA8d7Ss4c7bnw9AESs79TjcK6DypJkjGiFhq4TSVggUlRf4yjdqgZW1VFGQ\nyEeaqVQCfMCRpCpYCTbSk8YEXIYtA7K+Pmh8RZIktGMwJzV4YRucj0xNXLOChRgAGKMD5UhpksSg\nRfBqLqsSKWWTLNSqfioq+Cklo56Ba3q1UvhAl5QeIUIw67E44SLtdA59qwXOCSoVzpXAfHFUcS8v\nCSBgWzqEF0HbuuYax70AQHmJjYyYmpYbguUZsLBykUnjHK64xipzbjyjB7IRKpSOojlEw5iXkKog\n9dhQiALqKyJ1AapGzMLX6FsZJlREWhPEzDiKiIQb0H/NqLucTJpgXiiJFFUDSIkwogAO8RVOhMXt\n0eDDosF3wBukF6zKId5ZvMwpKUi6gsoGAFLltlDJEiZZYmX5DIu+w9ve9l0stHoM+uEx+5cn/OXF\nB5gUZUQUexZal4KBBDTa0nXGZJ3Gii4mBaEtwizSioCI0k7wPqCdi6JoqEbWVhzs7rISRfaLosDI\nBKklkyLHORflISvG4wtxlkLwk1yDln78whP86v/x73nP+34SgJ7uYW2QD0VI0iwjS9OAGm8+A4lz\nlslkwiOPPMrXPee5qE9/iq/88Yd48NhVdBpujrxv2R+8gDf+1Pu5IBN6ok1WSg7dtDkcU2NCdUNI\nKg8Cg5eaUXHIJK/wPmQ5JZbUpIymY9ppl7OnVhmND0i0btSWksTQ3w+ZycrKSvRrDgA8rQxazm6d\neTRwqNRcZ7FfM3REls+POkipn6se87S4GtQ0f5AB/MT5D/DPY4n62vGTFz/wlOd8uowTZln39UbY\nUyxJEnnp4mhlyzemLf7IPFyvNH0tMO2/pHx99DE+UgljIB/XkncOITSuCo8thUcpT5pJkiSAcaqq\noJxYVLOGY0YmBQ6PECnKS6ocji8Lvv87bwVgwq089oTn45+4j795+ArT3NI/nNLWRXNthYh0rajO\nNn+XbF/dRkaOcdK2LCVTZLrOZz6+woWHu/RajqWVksWVyB13QybTPoPhXpgnWeAowB7i/AE+7ieu\nOspDPjwcIk2KrSTj2EJCOs7eehI/KRlPx1S2pMorirKIXuAxQ7YW64KColEBmJQaSWIMrUjlM4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zP/za3zDD/1Nnnzyab76QWmneNtbv4yzD34pDuiPS1bWDvgn//h/QLvID33/dwNw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AkZKtLwWAmNPw5k255bWmiL+dYC4910Zp6T9YCdnlddKm06yJ9tpaWYmWK7rObJ9zHBtH2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\nyKqQV1lllVVWWeUekP8LtpKxDvHsdscAAAAASUVORK5CYII=\n","text/plain":["<matplotlib.figure.Figure at 0x7fa73d252eb8>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"i4XSyIoubCMY","colab_type":"text"},"cell_type":"markdown","source":["#### Build the model"]},{"metadata":{"id":"jODipXFDnDJ0","colab_type":"code","colab":{}},"cell_type":"code","source":["input_shape_img = (None, None, 3)\n","\n","img_input = Input(shape=input_shape_img)\n","roi_input = Input(shape=(None, 4))\n","\n","# define the base network (VGG here, can be Resnet50, Inception, etc)\n","shared_layers = nn_base(img_input, trainable=True)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"udTeQMVhfSzw","colab_type":"code","outputId":"11c3bda4-96e2-4658-ab6f-e5bf38519178","executionInfo":{"status":"ok","timestamp":1542545147976,"user_tz":-480,"elapsed":10162,"user":{"displayName":"Yinghan Xu","photoUrl":"https://lh3.googleusercontent.com/-ZH_vkc0a6g0/AAAAAAAAAAI/AAAAAAAAAAc/x8TPjkqmxys/s64/photo.jpg","userId":"13488364327507272606"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"cell_type":"code","source":["# define the RPN, built on the base layers\n","num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios) # 9\n","rpn = rpn_layer(shared_layers, num_anchors)\n","\n","classifier = classifier_layer(shared_layers, roi_input, C.num_rois, nb_classes=len(classes_count))\n","\n","model_rpn = Model(img_input, rpn[:2])\n","model_classifier = Model([img_input, roi_input], classifier)\n","\n","# this is a model that holds both the RPN and the classifier, used to load/save weights for the models\n","model_all = Model([img_input, roi_input], rpn[:2] + classifier)\n","\n","# Because the google colab can only run the session several hours one time (then you need to connect again), \n","# we need to save the model and load the model to continue training\n","if not os.path.isfile(C.model_path):\n","    #If this is the begin of the training, load the pre-traind base network such as vgg-16\n","    try:\n","        print('This is the first time of your training')\n","        print('loading weights from {}'.format(C.base_net_weights))\n","        model_rpn.load_weights(C.base_net_weights, by_name=True)\n","        model_classifier.load_weights(C.base_net_weights, by_name=True)\n","    except:\n","        print('Could not load pretrained model weights. Weights can be found in the keras application folder \\\n","            https://github.com/fchollet/keras/tree/master/keras/applications')\n","    \n","    # Create the record.csv file to record losses, acc and mAP\n","    record_df = pd.DataFrame(columns=['mean_overlapping_bboxes', 'class_acc', 'loss_rpn_cls', 'loss_rpn_regr', 'loss_class_cls', 'loss_class_regr', 'curr_loss', 'elapsed_time', 'mAP'])\n","else:\n","    # If this is a continued training, load the trained model from before\n","    print('Continue training based on previous trained model')\n","    print('Loading weights from {}'.format(C.model_path))\n","    model_rpn.load_weights(C.model_path, by_name=True)\n","    model_classifier.load_weights(C.model_path, by_name=True)\n","    \n","    # Load the records\n","    record_df = pd.read_csv(record_path)\n","\n","    r_mean_overlapping_bboxes = record_df['mean_overlapping_bboxes']\n","    r_class_acc = record_df['class_acc']\n","    r_loss_rpn_cls = record_df['loss_rpn_cls']\n","    r_loss_rpn_regr = record_df['loss_rpn_regr']\n","    r_loss_class_cls = record_df['loss_class_cls']\n","    r_loss_class_regr = record_df['loss_class_regr']\n","    r_curr_loss = record_df['curr_loss']\n","    r_elapsed_time = record_df['elapsed_time']\n","    r_mAP = record_df['mAP']\n","\n","    print('Already train %dK batches'% (len(record_df)))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Continue training based on previous trained model\n","Loading weights from drive/My Drive/AI/Faster_RCNN/model/model_frcnn_vgg.hdf5\n","Already train 114K batches\n"],"name":"stdout"}]},{"metadata":{"id":"-ULrg0V1soIR","colab_type":"code","colab":{}},"cell_type":"code","source":["optimizer = Adam(lr=1e-5)\n","optimizer_classifier = Adam(lr=1e-5)\n","model_rpn.compile(optimizer=optimizer, loss=[rpn_loss_cls(num_anchors), rpn_loss_regr(num_anchors)])\n","model_classifier.compile(optimizer=optimizer_classifier, loss=[class_loss_cls, class_loss_regr(len(classes_count)-1)], metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})\n","model_all.compile(optimizer='sgd', loss='mae')"],"execution_count":0,"outputs":[]},{"metadata":{"id":"Qz2BYzL6sqfu","colab_type":"code","colab":{}},"cell_type":"code","source":["# Training setting\n","total_epochs = len(record_df)\n","r_epochs = len(record_df)\n","\n","epoch_length = 1000\n","num_epochs = 40\n","iter_num = 0\n","\n","total_epochs += num_epochs\n","\n","losses = np.zeros((epoch_length, 5))\n","rpn_accuracy_rpn_monitor = []\n","rpn_accuracy_for_epoch = []\n","\n","if len(record_df)==0:\n","    best_loss = np.Inf\n","else:\n","    best_loss = np.min(r_curr_loss)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"JDysEDQA2DUz","colab_type":"code","outputId":"d38a5e1d-b44d-4645-b9f9-16802cc0f552","executionInfo":{"status":"ok","timestamp":1542544971410,"user_tz":-480,"elapsed":614,"user":{"displayName":"Yinghan Xu","photoUrl":"https://lh3.googleusercontent.com/-ZH_vkc0a6g0/AAAAAAAAAAI/AAAAAAAAAAc/x8TPjkqmxys/s64/photo.jpg","userId":"13488364327507272606"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["print(len(record_df))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0\n"],"name":"stdout"}]},{"metadata":{"id":"dRXtd5W30DRN","colab_type":"code","outputId":"438803d3-01c9-4818-859b-9a54e0243f93","executionInfo":{"status":"ok","timestamp":1542188908991,"user_tz":-480,"elapsed":1332624,"user":{"displayName":"Yinghan Xu","photoUrl":"https://lh3.googleusercontent.com/-ZH_vkc0a6g0/AAAAAAAAAAI/AAAAAAAAAAc/x8TPjkqmxys/s64/photo.jpg","userId":"13488364327507272606"}},"colab":{"base_uri":"https://localhost:8080/","height":4916}},"cell_type":"code","source":["start_time = time.time()\n","for epoch_num in range(num_epochs):\n","\n","    progbar = generic_utils.Progbar(epoch_length)\n","    print('Epoch {}/{}'.format(r_epochs + 1, total_epochs))\n","    \n","    r_epochs += 1\n","\n","    while True:\n","        try:\n","\n","            if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:\n","                mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor))/len(rpn_accuracy_rpn_monitor)\n","                rpn_accuracy_rpn_monitor = []\n","#                 print('Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'.format(mean_overlapping_bboxes, epoch_length))\n","                if mean_overlapping_bboxes == 0:\n","                    print('RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')\n","\n","            # Generate X (x_img) and label Y ([y_rpn_cls, y_rpn_regr])\n","            X, Y, img_data, debug_img, debug_num_pos = next(data_gen_train)\n","\n","            # Train rpn model and get loss value [_, loss_rpn_cls, loss_rpn_regr]\n","            loss_rpn = model_rpn.train_on_batch(X, Y)\n","\n","            # Get predicted rpn from rpn model [rpn_cls, rpn_regr]\n","            P_rpn = model_rpn.predict_on_batch(X)\n","\n","            # R: bboxes (shape=(300,4))\n","            # Convert rpn layer to roi bboxes\n","            R = rpn_to_roi(P_rpn[0], P_rpn[1], C, K.image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300)\n","            \n","            # note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format\n","            # X2: bboxes that iou > C.classifier_min_overlap for all gt bboxes in 300 non_max_suppression bboxes\n","            # Y1: one hot code for bboxes from above => x_roi (X)\n","            # Y2: corresponding labels and corresponding gt bboxes\n","            X2, Y1, Y2, IouS = calc_iou(R, img_data, C, class_mapping)\n","\n","            # If X2 is None means there are no matching bboxes\n","            if X2 is None:\n","                rpn_accuracy_rpn_monitor.append(0)\n","                rpn_accuracy_for_epoch.append(0)\n","                continue\n","            \n","            # Find out the positive anchors and negative anchors\n","            neg_samples = np.where(Y1[0, :, -1] == 1)\n","            pos_samples = np.where(Y1[0, :, -1] == 0)\n","\n","            if len(neg_samples) > 0:\n","                neg_samples = neg_samples[0]\n","            else:\n","                neg_samples = []\n","\n","            if len(pos_samples) > 0:\n","                pos_samples = pos_samples[0]\n","            else:\n","                pos_samples = []\n","\n","            rpn_accuracy_rpn_monitor.append(len(pos_samples))\n","            rpn_accuracy_for_epoch.append((len(pos_samples)))\n","\n","            if C.num_rois > 1:\n","                # If number of positive anchors is larger than 4//2 = 2, randomly choose 2 pos samples\n","                if len(pos_samples) < C.num_rois//2:\n","                    selected_pos_samples = pos_samples.tolist()\n","                else:\n","                    selected_pos_samples = np.random.choice(pos_samples, C.num_rois//2, replace=False).tolist()\n","                \n","                # Randomly choose (num_rois - num_pos) neg samples\n","                try:\n","                    selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=False).tolist()\n","                except:\n","                    selected_neg_samples = np.random.choice(neg_samples, C.num_rois - len(selected_pos_samples), replace=True).tolist()\n","                \n","                # Save all the pos and neg samples in sel_samples\n","                sel_samples = selected_pos_samples + selected_neg_samples\n","            else:\n","                # in the extreme case where num_rois = 1, we pick a random pos or neg sample\n","                selected_pos_samples = pos_samples.tolist()\n","                selected_neg_samples = neg_samples.tolist()\n","                if np.random.randint(0, 2):\n","                    sel_samples = random.choice(neg_samples)\n","                else:\n","                    sel_samples = random.choice(pos_samples)\n","\n","            # training_data: [X, X2[:, sel_samples, :]]\n","            # labels: [Y1[:, sel_samples, :], Y2[:, sel_samples, :]]\n","            #  X                     => img_data resized image\n","            #  X2[:, sel_samples, :] => num_rois (4 in here) bboxes which contains selected neg and pos\n","            #  Y1[:, sel_samples, :] => one hot encode for num_rois bboxes which contains selected neg and pos\n","            #  Y2[:, sel_samples, :] => labels and gt bboxes for num_rois bboxes which contains selected neg and pos\n","            loss_class = model_classifier.train_on_batch([X, X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]])\n","\n","            losses[iter_num, 0] = loss_rpn[1]\n","            losses[iter_num, 1] = loss_rpn[2]\n","\n","            losses[iter_num, 2] = loss_class[1]\n","            losses[iter_num, 3] = loss_class[2]\n","            losses[iter_num, 4] = loss_class[3]\n","\n","            iter_num += 1\n","\n","            progbar.update(iter_num, [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])),\n","                                      ('final_cls', np.mean(losses[:iter_num, 2])), ('final_regr', np.mean(losses[:iter_num, 3]))])\n","\n","            if iter_num == epoch_length:\n","                loss_rpn_cls = np.mean(losses[:, 0])\n","                loss_rpn_regr = np.mean(losses[:, 1])\n","                loss_class_cls = np.mean(losses[:, 2])\n","                loss_class_regr = np.mean(losses[:, 3])\n","                class_acc = np.mean(losses[:, 4])\n","\n","                mean_overlapping_bboxes = float(sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)\n","                rpn_accuracy_for_epoch = []\n","\n","                if C.verbose:\n","                    print('Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))\n","                    print('Classifier accuracy for bounding boxes from RPN: {}'.format(class_acc))\n","                    print('Loss RPN classifier: {}'.format(loss_rpn_cls))\n","                    print('Loss RPN regression: {}'.format(loss_rpn_regr))\n","                    print('Loss Detector classifier: {}'.format(loss_class_cls))\n","                    print('Loss Detector regression: {}'.format(loss_class_regr))\n","                    print('Total loss: {}'.format(loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr))\n","                    print('Elapsed time: {}'.format(time.time() - start_time))\n","                    elapsed_time = (time.time()-start_time)/60\n","\n","                curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr\n","                iter_num = 0\n","                start_time = time.time()\n","\n","                if curr_loss < best_loss:\n","                    if C.verbose:\n","                        print('Total loss decreased from {} to {}, saving weights'.format(best_loss,curr_loss))\n","                    best_loss = curr_loss\n","                    model_all.save_weights(C.model_path)\n","\n","                new_row = {'mean_overlapping_bboxes':round(mean_overlapping_bboxes, 3), \n","                           'class_acc':round(class_acc, 3), \n","                           'loss_rpn_cls':round(loss_rpn_cls, 3), \n","                           'loss_rpn_regr':round(loss_rpn_regr, 3), \n","                           'loss_class_cls':round(loss_class_cls, 3), \n","                           'loss_class_regr':round(loss_class_regr, 3), \n","                           'curr_loss':round(curr_loss, 3), \n","                           'elapsed_time':round(elapsed_time, 3), \n","                           'mAP': 0}\n","\n","                record_df = record_df.append(new_row, ignore_index=True)\n","                record_df.to_csv(record_path, index=0)\n","\n","                break\n","\n","        except Exception as e:\n","            print('Exception: {}'.format(e))\n","            continue\n","\n","print('Training complete, exiting.')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epoch 87/126\n","1000/1000 [==============================] - 933s 933ms/step - rpn_cls: 0.8438 - rpn_regr: 0.2302 - final_cls: 0.4520 - final_regr: 0.2268\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.26321036889332\n","Classifier accuracy for bounding boxes from RPN: 0.807\n","Loss RPN classifier: 0.8722642721683797\n","Loss RPN regression: 0.23128215429116972\n","Loss Detector classifier: 0.446441008906977\n","Loss Detector regression: 0.23175630393996835\n","Total loss: 1.7817437393064948\n","Elapsed time: 932.6565265655518\n","Epoch 88/126\n","1000/1000 [==============================] - 785s 785ms/step - rpn_cls: 0.9270 - rpn_regr: 0.2315 - final_cls: 0.4147 - final_regr: 0.2248\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 11.53227408142999\n","Classifier accuracy for bounding boxes from RPN: 0.81425\n","Loss RPN classifier: 0.87854375148419\n","Loss RPN regression: 0.23205739994451868\n","Loss Detector classifier: 0.4183896440564631\n","Loss Detector regression: 0.23319237146084196\n","Total loss: 1.7621831669460137\n","Elapsed time: 785.0421531200409\n","Epoch 89/126\n","1000/1000 [==============================] - 737s 737ms/step - rpn_cls: 0.9438 - rpn_regr: 0.2379 - final_cls: 0.4652 - final_regr: 0.2445\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.395812562313061\n","Classifier accuracy for bounding boxes from RPN: 0.80775\n","Loss RPN classifier: 0.9155260486609184\n","Loss RPN regression: 0.22743836374177046\n","Loss Detector classifier: 0.4338727388291154\n","Loss Detector regression: 0.22870131808705627\n","Total loss: 1.8055384693188605\n","Elapsed time: 737.3596696853638\n","Epoch 90/126\n","1000/1000 [==============================] - 687s 687ms/step - rpn_cls: 1.0162 - rpn_regr: 0.2298 - final_cls: 0.4525 - final_regr: 0.2309\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 11.679960119641077\n","Classifier accuracy for bounding boxes from RPN: 0.80575\n","Loss RPN classifier: 0.986764288362554\n","Loss RPN regression: 0.2323578881379217\n","Loss Detector classifier: 0.45484118838390714\n","Loss Detector regression: 0.23057722708582878\n","Total loss: 1.9045405919702116\n","Elapsed time: 686.8345861434937\n","Epoch 91/126\n","1000/1000 [==============================] - 697s 697ms/step - rpn_cls: 0.7980 - rpn_regr: 0.2185 - final_cls: 0.4256 - final_regr: 0.2251\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.281592039800994\n","Classifier accuracy for bounding boxes from RPN: 0.815\n","Loss RPN classifier: 0.8477030687925966\n","Loss RPN regression: 0.2275357511814218\n","Loss Detector classifier: 0.4232247541067709\n","Loss Detector regression: 0.23539060649741442\n","Total loss: 1.7338541805782037\n","Elapsed time: 696.9466845989227\n","Total loss decreased from 1.736 to 1.7338541805782037, saving weights\n","Epoch 92/126\n","1000/1000 [==============================] - 678s 678ms/step - rpn_cls: 0.8462 - rpn_regr: 0.2207 - final_cls: 0.4237 - final_regr: 0.2206\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.264471057884231\n","Classifier accuracy for bounding boxes from RPN: 0.815\n","Loss RPN classifier: 0.8911424900373224\n","Loss RPN regression: 0.2226152374977246\n","Loss Detector classifier: 0.4318747027449226\n","Loss Detector regression: 0.220734876235947\n","Total loss: 1.7663673065159164\n","Elapsed time: 681.6890978813171\n","Epoch 93/126\n","1000/1000 [==============================] - 682s 682ms/step - rpn_cls: 0.9654 - rpn_regr: 0.2333 - final_cls: 0.4326 - final_regr: 0.2460\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 11.714995034756702\n","Classifier accuracy for bounding boxes from RPN: 0.81375\n","Loss RPN classifier: 0.9607130975700974\n","Loss RPN regression: 0.22782757084256444\n","Loss Detector classifier: 0.42642463095804306\n","Loss Detector regression: 0.23244121629465372\n","Total loss: 1.8474065156653585\n","Elapsed time: 682.004195690155\n","Epoch 94/126\n","1000/1000 [==============================] - 679s 679ms/step - rpn_cls: 0.8438 - rpn_regr: 0.2270 - final_cls: 0.4137 - final_regr: 0.2231\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.526892430278885\n","Classifier accuracy for bounding boxes from RPN: 0.81375\n","Loss RPN classifier: 0.8844943608916693\n","Loss RPN regression: 0.23212345585296862\n","Loss Detector classifier: 0.41998655918764416\n","Loss Detector regression: 0.22275175954960286\n","Total loss: 1.759356135481885\n","Elapsed time: 679.2204189300537\n","Epoch 95/126\n","1000/1000 [==============================] - 680s 680ms/step - rpn_cls: 0.8441 - rpn_regr: 0.2180 - final_cls: 0.4444 - final_regr: 0.2331\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 11.878727634194831\n","Classifier accuracy for bounding boxes from RPN: 0.805\n","Loss RPN classifier: 0.9111342019203675\n","Loss RPN regression: 0.22692135337973013\n","Loss Detector classifier: 0.43058419609848986\n","Loss Detector regression: 0.22657714950852095\n","Total loss: 1.7952169009071084\n","Elapsed time: 680.4641258716583\n","Epoch 96/126\n","1000/1000 [==============================] - 679s 679ms/step - rpn_cls: 0.9537 - rpn_regr: 0.2343 - final_cls: 0.4240 - final_regr: 0.2367\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.229083665338646\n","Classifier accuracy for bounding boxes from RPN: 0.82625\n","Loss RPN classifier: 0.9079539056703686\n","Loss RPN regression: 0.2323591099884361\n","Loss Detector classifier: 0.41412826041040407\n","Loss Detector regression: 0.23161866784491575\n","Total loss: 1.7860599439141243\n","Elapsed time: 679.4896247386932\n","Epoch 97/126\n","1000/1000 [==============================] - 672s 672ms/step - rpn_cls: 0.8953 - rpn_regr: 0.2152 - final_cls: 0.4052 - final_regr: 0.2197\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.352647352647352\n","Classifier accuracy for bounding boxes from RPN: 0.823\n","Loss RPN classifier: 0.9031416995510066\n","Loss RPN regression: 0.224318673487287\n","Loss Detector classifier: 0.4130580574554624\n","Loss Detector regression: 0.22467246198188515\n","Total loss: 1.7651908924756414\n","Elapsed time: 671.9303152561188\n","Epoch 98/126\n","1000/1000 [==============================] - 671s 671ms/step - rpn_cls: 0.9033 - rpn_regr: 0.2188 - final_cls: 0.4170 - final_regr: 0.2149\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.067393458870168\n","Classifier accuracy for bounding boxes from RPN: 0.81575\n","Loss RPN classifier: 0.8717304479568506\n","Loss RPN regression: 0.21930668960663025\n","Loss Detector classifier: 0.4265757374609075\n","Loss Detector regression: 0.21874751579761506\n","Total loss: 1.7363603908220033\n","Elapsed time: 670.6827230453491\n","Epoch 99/126\n","1000/1000 [==============================] - 675s 675ms/step - rpn_cls: 0.8869 - rpn_regr: 0.2259 - final_cls: 0.4205 - final_regr: 0.2099\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.609390609390609\n","Classifier accuracy for bounding boxes from RPN: 0.81825\n","Loss RPN classifier: 0.9482178036992093\n","Loss RPN regression: 0.22202652960573324\n","Loss Detector classifier: 0.4130795040359959\n","Loss Detector regression: 0.21526918029086664\n","Total loss: 1.798593017631805\n","Elapsed time: 674.8599791526794\n","Epoch 100/126\n","1000/1000 [==============================] - 671s 671ms/step - rpn_cls: 0.9833 - rpn_regr: 0.2298 - final_cls: 0.4297 - final_regr: 0.2240\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 11.641153081510934\n","Classifier accuracy for bounding boxes from RPN: 0.8055\n","Loss RPN classifier: 0.9223214232672258\n","Loss RPN regression: 0.22673636169638484\n","Loss Detector classifier: 0.4253467804697284\n","Loss Detector regression: 0.22203194227907808\n","Total loss: 1.796436507712417\n","Elapsed time: 670.7692708969116\n","Epoch 101/126\n","1000/1000 [==============================] - 671s 671ms/step - rpn_cls: 0.8975 - rpn_regr: 0.2175 - final_cls: 0.4354 - final_regr: 0.2293\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.685942173479562\n","Classifier accuracy for bounding boxes from RPN: 0.8125\n","Loss RPN classifier: 0.8668885441722987\n","Loss RPN regression: 0.21832428600871936\n","Loss Detector classifier: 0.42470300570610925\n","Loss Detector regression: 0.22119836130179465\n","Total loss: 1.731114197188922\n","Elapsed time: 670.9476668834686\n","Total loss decreased from 1.7338541805782037 to 1.731114197188922, saving weights\n","Epoch 102/126\n","1000/1000 [==============================] - 669s 669ms/step - rpn_cls: 0.8904 - rpn_regr: 0.2423 - final_cls: 0.4214 - final_regr: 0.2204\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 11.94820717131474\n","Classifier accuracy for bounding boxes from RPN: 0.81475\n","Loss RPN classifier: 0.7872176631528773\n","Loss RPN regression: 0.22854730435018428\n","Loss Detector classifier: 0.41807236317757634\n","Loss Detector regression: 0.21610773099400102\n","Total loss: 1.649945061674639\n","Elapsed time: 677.5893723964691\n","Total loss decreased from 1.731114197188922 to 1.649945061674639, saving weights\n","Epoch 103/126\n","1000/1000 [==============================] - 671s 671ms/step - rpn_cls: 0.8626 - rpn_regr: 0.2196 - final_cls: 0.4249 - final_regr: 0.2208\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.276616915422885\n","Classifier accuracy for bounding boxes from RPN: 0.81775\n","Loss RPN classifier: 0.8613967324917848\n","Loss RPN regression: 0.22431252040481195\n","Loss Detector classifier: 0.42475868741355954\n","Loss Detector regression: 0.21907370595261455\n","Total loss: 1.729541646262771\n","Elapsed time: 679.4307219982147\n","Epoch 104/126\n","1000/1000 [==============================] - 668s 668ms/step - rpn_cls: 0.7840 - rpn_regr: 0.2260 - final_cls: 0.4177 - final_regr: 0.2071\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.474\n","Classifier accuracy for bounding boxes from RPN: 0.81925\n","Loss RPN classifier: 0.8067858474111436\n","Loss RPN regression: 0.22261562213115393\n","Loss Detector classifier: 0.4136864809736326\n","Loss Detector regression: 0.21846730195730923\n","Total loss: 1.6615552524732395\n","Elapsed time: 668.0380980968475\n","Epoch 105/126\n"," 861/1000 [========================>.....] - ETA: 1:32 - rpn_cls: 0.7688 - rpn_regr: 0.2205 - final_cls: 0.4302 - final_regr: 0.2273Exception: a must be non-empty\n","1000/1000 [==============================] - 667s 667ms/step - rpn_cls: 0.7733 - rpn_regr: 0.2204 - final_cls: 0.4306 - final_regr: 0.2268\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.08820614469772\n","Classifier accuracy for bounding boxes from RPN: 0.8095\n","Loss RPN classifier: 0.7959171475692999\n","Loss RPN regression: 0.222007484203903\n","Loss Detector classifier: 0.4377380418071407\n","Loss Detector regression: 0.22211360837356187\n","Total loss: 1.6777762819539057\n","Elapsed time: 667.2070758342743\n","Epoch 106/126\n","1000/1000 [==============================] - 674s 674ms/step - rpn_cls: 0.7959 - rpn_regr: 0.2154 - final_cls: 0.4169 - final_regr: 0.2184\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.786640079760717\n","Classifier accuracy for bounding boxes from RPN: 0.8215\n","Loss RPN classifier: 0.846168461129957\n","Loss RPN regression: 0.21744726059911773\n","Loss Detector classifier: 0.4037010812334504\n","Loss Detector regression: 0.2158492755345069\n","Total loss: 1.6831660784970321\n","Elapsed time: 673.8256871700287\n","Epoch 107/126\n","1000/1000 [==============================] - 670s 670ms/step - rpn_cls: 0.8442 - rpn_regr: 0.2295 - final_cls: 0.4116 - final_regr: 0.2309\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 11.70506454816286\n","Classifier accuracy for bounding boxes from RPN: 0.82025\n","Loss RPN classifier: 0.8467203306242823\n","Loss RPN regression: 0.22447337504476308\n","Loss Detector classifier: 0.41587843135683034\n","Loss Detector regression: 0.2292122566020116\n","Total loss: 1.7162843936278875\n","Elapsed time: 669.6604249477386\n","Epoch 108/126\n","1000/1000 [==============================] - 675s 675ms/step - rpn_cls: 0.7789 - rpn_regr: 0.2112 - final_cls: 0.4201 - final_regr: 0.2155\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.577534791252486\n","Classifier accuracy for bounding boxes from RPN: 0.82175\n","Loss RPN classifier: 0.7678409469053944\n","Loss RPN regression: 0.2149370092410827\n","Loss Detector classifier: 0.4027629663187836\n","Loss Detector regression: 0.21133415527641775\n","Total loss: 1.5968750777416787\n","Elapsed time: 675.1620604991913\n","Total loss decreased from 1.649945061674639 to 1.5968750777416787, saving weights\n","Epoch 109/126\n","1000/1000 [==============================] - 670s 670ms/step - rpn_cls: 0.9065 - rpn_regr: 0.2128 - final_cls: 0.4076 - final_regr: 0.2127\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.132470119521912\n","Classifier accuracy for bounding boxes from RPN: 0.81825\n","Loss RPN classifier: 0.8441924907343343\n","Loss RPN regression: 0.22226721304282546\n","Loss Detector classifier: 0.4123301836922765\n","Loss Detector regression: 0.21703247728943825\n","Total loss: 1.6958223647588744\n","Elapsed time: 677.9798049926758\n","Epoch 110/126\n","1000/1000 [==============================] - 670s 670ms/step - rpn_cls: 0.7749 - rpn_regr: 0.2036 - final_cls: 0.4158 - final_regr: 0.2197\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.329038652130823\n","Classifier accuracy for bounding boxes from RPN: 0.82275\n","Loss RPN classifier: 0.7899966503038468\n","Loss RPN regression: 0.2119592136682477\n","Loss Detector classifier: 0.4226520763477893\n","Loss Detector regression: 0.21991704475926235\n","Total loss: 1.6445249850791461\n","Elapsed time: 670.288943529129\n","Epoch 111/126\n","1000/1000 [==============================] - 670s 670ms/step - rpn_cls: 0.8496 - rpn_regr: 0.2320 - final_cls: 0.4058 - final_regr: 0.2131\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.639360639360639\n","Classifier accuracy for bounding boxes from RPN: 0.82125\n","Loss RPN classifier: 0.8730854176077889\n","Loss RPN regression: 0.2181670014592819\n","Loss Detector classifier: 0.4105416601966208\n","Loss Detector regression: 0.21089217773801647\n","Total loss: 1.7126862570017078\n","Elapsed time: 670.4270896911621\n","Epoch 112/126\n","1000/1000 [==============================] - 669s 669ms/step - rpn_cls: 0.7661 - rpn_regr: 0.2172 - final_cls: 0.3908 - final_regr: 0.2244\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 11.795454545454545\n","Classifier accuracy for bounding boxes from RPN: 0.83025\n","Loss RPN classifier: 0.8015139575901218\n","Loss RPN regression: 0.21463534024066758\n","Loss Detector classifier: 0.39076291947363645\n","Loss Detector regression: 0.22494742555939592\n","Total loss: 1.6318596428638217\n","Elapsed time: 668.8377795219421\n","Epoch 113/126\n","1000/1000 [==============================] - 671s 671ms/step - rpn_cls: 0.9086 - rpn_regr: 0.2213 - final_cls: 0.3922 - final_regr: 0.2063\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.873505976095618\n","Classifier accuracy for bounding boxes from RPN: 0.81925\n","Loss RPN classifier: 0.8810007151811414\n","Loss RPN regression: 0.21984351762349252\n","Loss Detector classifier: 0.3939479991452536\n","Loss Detector regression: 0.20736837571440264\n","Total loss: 1.7021606076642903\n","Elapsed time: 671.0422077178955\n","Epoch 114/126\n","1000/1000 [==============================] - 667s 667ms/step - rpn_cls: 0.8282 - rpn_regr: 0.2094 - final_cls: 0.3953 - final_regr: 0.2172\n","Mean number of bounding boxes from RPN overlapping ground truth boxes: 12.243781094527364\n","Classifier accuracy for bounding boxes from RPN: 0.8225\n","Loss RPN classifier: 0.8145011890659286\n","Loss RPN regression: 0.21681321294745431\n","Loss Detector classifier: 0.4022708480304234\n","Loss Detector regression: 0.21461959836632014\n","Total loss: 1.6482048484101264\n","Elapsed time: 667.3746252059937\n","Epoch 115/126\n"," 389/1000 [==========>...................] - ETA: 6:51 - rpn_cls: 0.7720 - rpn_regr: 0.2066 - final_cls: 0.3945 - final_regr: 0.2086"],"name":"stdout"}]},{"metadata":{"id":"Kt-1Grs90oD3","colab_type":"code","outputId":"bc9b57eb-e573-447f-a13e-c264b4500f5c","executionInfo":{"status":"ok","timestamp":1542545625897,"user_tz":-480,"elapsed":4761,"user":{"displayName":"Yinghan Xu","photoUrl":"https://lh3.googleusercontent.com/-ZH_vkc0a6g0/AAAAAAAAAAI/AAAAAAAAAAc/x8TPjkqmxys/s64/photo.jpg","userId":"13488364327507272606"}},"colab":{"base_uri":"https://localhost:8080/","height":1316}},"cell_type":"code","source":["plt.figure(figsize=(15,5))\n","plt.subplot(1,2,1)\n","plt.plot(np.arange(0, r_epochs), record_df['mean_overlapping_bboxes'], 'r')\n","plt.title('mean_overlapping_bboxes')\n","plt.subplot(1,2,2)\n","plt.plot(np.arange(0, r_epochs), record_df['class_acc'], 'r')\n","plt.title('class_acc')\n","\n","plt.show()\n","\n","plt.figure(figsize=(15,5))\n","plt.subplot(1,2,1)\n","plt.plot(np.arange(0, r_epochs), record_df['loss_rpn_cls'], 'r')\n","plt.title('loss_rpn_cls')\n","plt.subplot(1,2,2)\n","plt.plot(np.arange(0, r_epochs), record_df['loss_rpn_regr'], 'r')\n","plt.title('loss_rpn_regr')\n","plt.show()\n","\n","\n","plt.figure(figsize=(15,5))\n","plt.subplot(1,2,1)\n","plt.plot(np.arange(0, r_epochs), record_df['loss_class_cls'], 'r')\n","plt.title('loss_class_cls')\n","plt.subplot(1,2,2)\n","plt.plot(np.arange(0, r_epochs), record_df['loss_class_regr'], 'r')\n","plt.title('loss_class_regr')\n","plt.show()\n","\n","plt.plot(np.arange(0, r_epochs), record_df['curr_loss'], 'r')\n","plt.title('total_loss')\n","plt.show()\n","\n","# plt.figure(figsize=(15,5))\n","# plt.subplot(1,2,1)\n","# plt.plot(np.arange(0, r_epochs), record_df['curr_loss'], 'r')\n","# plt.title('total_loss')\n","# plt.subplot(1,2,2)\n","# plt.plot(np.arange(0, r_epochs), record_df['elapsed_time'], 'r')\n","# plt.title('elapsed_time')\n","# plt.show()\n","\n","# plt.title('loss')\n","# plt.plot(np.arange(0, r_epochs), record_df['loss_rpn_cls'], 'b')\n","# plt.plot(np.arange(0, r_epochs), record_df['loss_rpn_regr'], 'g')\n","# plt.plot(np.arange(0, r_epochs), record_df['loss_class_cls'], 'r')\n","# plt.plot(np.arange(0, r_epochs), record_df['loss_class_regr'], 'c')\n","# # plt.plot(np.arange(0, r_epochs), record_df['curr_loss'], 'm')\n","# plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA2UAAAE+CAYAAAANoLhOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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oU2asy7jnlnV9h6Aog2BdGYBqpi+GGn3oOnJOtiX0/S2NSJcS6G/muepayqIlJiJnZVnu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TQFMRESkLtnteM7pi/2XnVoNUUSkFoVdACMqikCz5gpgIiIidcxzbn9AwxBFRGpT+AUwwN/yeOy/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\n927KlSuHw+HAYrFw7733snPnzvwatrhGFZb5EWSO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PNN+zf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\nPBn0eC7+G6aqWJ94nIi2LXMOKoUQQoirwNmzN76K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8u3IzHRX+XvWJpboj99P/Iw3wTTx9rsyfBNWcTEZJx8Lpsmu1T+Dy7XPYUZ+Hqm9LsK5aiW5U16j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\nyIqIiFQThayIiEg1+X+6BRxhe0hwDgAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure 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